We present a causal speech enhancement model working on the
raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with
skip-connections. It is optimized on both time and frequency
domains, using multiple loss functions. Empirical evidence
shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises,
as well as room reverb. Additionally, we suggest a set of
data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard
benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working
directly on the raw waveform.
Index Terms: Speech enhancement, speech denoising, neural
networks, raw waveform
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
Design and implementation of different audio restoration techniques for audio...eSAT Journals
Abstract
Audio signals are corrupted with many types of distortions. Major audio distortions are categorized into Globalized and
Localized distortions. Localized distortion includes clipping and clicks where only certain samples are affected and globalized
distortions include broadband noise where complete bandwidth is consumed by noise. Audio restoration is a technique for giving
back the audio signals from these distortions. In this paper, audio restoration techniques for removing clipping, clicks and
broadband noise are put forwarded. Recent approaches to solving audio restoration problem is with respect to sparse
representation algorithms. Clipping distortion is addressed with a Sparse representation framework, it is treated as a reverse
problem, where the distorted samples is estimated from the surrounding undistorted samples, they are embedded in frame based
scheme, and reconstructed by using an overlap add method in conjunction with OMP algorithm and Gabor/DCT dictionary for
modelling audio signals. Broadband denoising is done by using spectral subtraction and Click removal is done by using an
adaptive filter method as the first step. Performance measures are done based on perception, average SNR calculation and
defined parameter variations. This paper also targeting towards the software and hardware implementation of the restoration
methods using TMS320C6713 DSK kit with help of tools mainly MATLAB and Code Composer studio.
Key Words: Audio Distortions, OMP algorithm, Gabor/DCT dictionary, TMS320C6713DSK
Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
Performance Analysis of Fractional Sample Rate Converter Using Audio Applicat...iosrjce
Fractional rate converters which are generally used for many applications with different frequencies
and are an essential part of communication systems. In this paper fractional rate converter with use of both
FIR and Nyquist FIR have been compared and analyzed. Its implementation can be easily found in the
developing communication systems, but here results are taken for audio applications. The proposed design and
analysis have been developed with the help of MATLAB with order 50 for FIR and 71 for Nyquist, sampling
frequency 48000Hz. The filters are then interpolated by an interpolation factor 2 and decimated by a decimation
factor of 3. The cost implementation of both has been taken into consideration and a result is drawn which
concludes that fractional rate converter for Nyquist FIR filter much more cost effective as compared to the
fractional rate converter for FIR filter
Compressive Sensing in Speech from LPC using Gradient Projection for Sparse R...IJERA Editor
This paper presents compressive sensing technique used for speech reconstruction using linear predictive coding because the
speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily.
This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete
points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient
Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The
experiment is done and it is observed that the performance is better for compressive sensing than the DCT.
Data Compression using Multiple Transformation Techniques for Audio Applicati...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Adaptive Channel Equalization using Multilayer Perceptron Neural Networks wit...IOSRJVSP
This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN). The channel equalizer is modelled as a non-linear Multilayer Perceptron (MLP) structure. The Back Propagation (BP) algorithm is used to optimize the synaptic weights of the equalizer during the training mode. In the typical BP algorithm, the error signal is propagated from the output layer to the input layer while the learning rate parameter is held constant. In this study, the BP algorithm is modified so as to allow for the learning rate to be variable at each iteration and this achieves a faster convergence. The proposed algorithm is used to train the MLP based decision feedback equalizer (DFE) for time dispersive ISI channels. The equalizer is tested for a random input sequence of BPSK signals and its performance analysed in terms of the Bit Error Rates and speed of convergence. Simulation results show that the proposed algorithm improves the Bit Error Rate (BER) and rate of convergence.
A Novel Uncertainty Parameter SR ( Signal to Residual Spectrum Ratio ) Evalua...sipij
Usually, hearing impaired people use hearing aids which are implemented with speech enhancement
algorithms. Estimation of speech and estimation of nose are the components in single channel speech
enhancement system. The main objective of any speech enhancement algorithm is estimation of noise power
spectrum for non stationary environment. VAD (Voice Activity Detector) is used to identify speech pauses
and during these pauses only estimation of noise. MMSE (Minimum Mean Square Error) speech
enhancement algorithm did not enhance the intelligibility, quality and listener fatigues are the perceptual
aspects of speech. Novel evaluation approach SR (Signal to Residual spectrum ratio) based on uncertainty
parameter introduced for the benefits of hearing impaired people in non stationary environments to control
distortions. By estimation and updating of noise based on division of original pure signal into three parts
such as pure speech, quasi speech and non speech frames based on multiple threshold conditions. Different
values of SR and LLR demonstrate the amount of attenuation and amplification distortions. The proposed
method will compared with any one method WAT(Weighted Average Technique) Hence by using
parameters SR (signal to residual spectrum ratio) and LLR (log like hood ratio), MMSE (Minim Mean
Square Error) in terms of segmented SNR and LLR.
Real Time System Identification of Speech Signal Using Tms320c6713IOSRJVSP
In this paper real time system identification is implemented on TMS320C6713 for speech signal using standard IEEE sentence (SP23) of NOIZEUS database with different types of real world noises at different level SNR taken from AURORA data base. The performance is measured in terms of signal to noise ratio (SNR) improvement. The implementation is done with “C’ program for least mean square (LMS) and recursive least square (RLS) algorithms and processed using DSP processor with Code composer studio.
Performance of Matching Algorithmsfor Signal Approximationiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An Effective Approach for Chinese Speech Recognition on Small Size of Vocabularysipij
In this paper, an effective approach for Chinese speech recognition on small vocabulary size is proposed - the independent speech recognition of Chinese words based on Hidden Markov Model (HMM). The features of speech words are generated by sub-syllable of Chinese characters. Total 640 speech samples are recorded by 4 native males and 4 females with frequently speaking ability. The preliminary results of inside and outside testing achieve 89.6% and 77.5%, respectively. To improve the performance, keyword spotting criterion is applied into our system. The final precision rates for inside and outside testing in average achieve 92.7% and 83.8%. The results prove that the approach for Chinese speech recognition on small vocabulary is effective.
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
Investing in AI transformation today
The modern business advantage: Uncovering deep insights with AI
Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage.
AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive
operational efficiency, transform their products, and better serve their customers
Last year’s Global Risks Report warned of a world
that would not easily rebound from continued
shocks. As 2024 begins, the 19th edition of
the report is set against a backdrop of rapidly
accelerating technological change and economic
uncertainty, as the world is plagued by a duo of
dangerous crises: climate and conflict.
Underlying geopolitical tensions combined with the
eruption of active hostilities in multiple regions is
contributing to an unstable global order characterized
by polarizing narratives, eroding trust and insecurity.
At the same time, countries are grappling with the
impacts of record-breaking extreme weather, as
climate-change adaptation efforts and resources
fall short of the type, scale and intensity of climaterelated events already taking place. Cost-of-living
pressures continue to bite, amidst persistently
elevated inflation and interest rates and continued
economic uncertainty in much of the world.
Despondent headlines are borderless, shared
regularly and widely, and a sense of frustration at
the status quo is increasingly palpable. Together,
this leaves ample room for accelerating risks – like
misinformation and disinformation – to propagate
in societies that have already been politically and
economically weakened in recent years.
Just as natural ecosystems can be pushed to the
limit and become something fundamentally new;
such systemic shifts are also taking place across
other spheres: geostrategic, demographic and
technological. This year, we explore the rise of global
risks against the backdrop of these “structural
forces” as well as the tectonic clashes between
them. The next set of global conditions may not
necessarily be better or worse than the last, but the
transition will not be an easy one.
The report explores the global risk landscape in this
phase of transition and governance systems being
stretched beyond their limit. It analyses the most
severe perceived risks to economies and societies
over two and 10 years, in the context of these
influential forces. Could we catapult to a 3°C world
as the impacts of climate change intrinsically rewrite
the planet? Have we reached the peak of human
development for large parts of the global population,
given deteriorating debt and geo-economic
conditions? Could we face an explosion of criminality
and corruption that feeds on more fragile states and
more vulnerable populations? Will an “arms race” in
experimental technologies present existential threats
to humanity?
These transnational risks will become harder to
handle as global cooperation erodes. In this year’s
Global Risks Perception Survey, two-thirds of
respondents predict that a multipolar order will
dominate in the next 10 years, as middle and
great powers set and enforce – but also contest
- current rules and norms. The report considers
the implications of this fragmented world, where
preparedness for global risks is ever more critical but
is hindered by lack o
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Audio signals are corrupted with many types of distortions. Major audio distortions are categorized into Globalized and
Localized distortions. Localized distortion includes clipping and clicks where only certain samples are affected and globalized
distortions include broadband noise where complete bandwidth is consumed by noise. Audio restoration is a technique for giving
back the audio signals from these distortions. In this paper, audio restoration techniques for removing clipping, clicks and
broadband noise are put forwarded. Recent approaches to solving audio restoration problem is with respect to sparse
representation algorithms. Clipping distortion is addressed with a Sparse representation framework, it is treated as a reverse
problem, where the distorted samples is estimated from the surrounding undistorted samples, they are embedded in frame based
scheme, and reconstructed by using an overlap add method in conjunction with OMP algorithm and Gabor/DCT dictionary for
modelling audio signals. Broadband denoising is done by using spectral subtraction and Click removal is done by using an
adaptive filter method as the first step. Performance measures are done based on perception, average SNR calculation and
defined parameter variations. This paper also targeting towards the software and hardware implementation of the restoration
methods using TMS320C6713 DSK kit with help of tools mainly MATLAB and Code Composer studio.
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Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.
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In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
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Fractional rate converters which are generally used for many applications with different frequencies
and are an essential part of communication systems. In this paper fractional rate converter with use of both
FIR and Nyquist FIR have been compared and analyzed. Its implementation can be easily found in the
developing communication systems, but here results are taken for audio applications. The proposed design and
analysis have been developed with the help of MATLAB with order 50 for FIR and 71 for Nyquist, sampling
frequency 48000Hz. The filters are then interpolated by an interpolation factor 2 and decimated by a decimation
factor of 3. The cost implementation of both has been taken into consideration and a result is drawn which
concludes that fractional rate converter for Nyquist FIR filter much more cost effective as compared to the
fractional rate converter for FIR filter
Compressive Sensing in Speech from LPC using Gradient Projection for Sparse R...IJERA Editor
This paper presents compressive sensing technique used for speech reconstruction using linear predictive coding because the
speech is more sparse in LPC. DCT of a speech is taken and the DCT points of sparse speech are thrown away arbitrarily.
This is achieved by making some point in DCT domain to be zero by multiplying with mask functions. From the incomplete
points in DCT domain, the original speech is reconstructed using compressive sensing and the tool used is Gradient
Projection for Sparse Reconstruction. The performance of the result is compared with direct IDCT subjectively. The
experiment is done and it is observed that the performance is better for compressive sensing than the DCT.
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IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN). The channel equalizer is modelled as a non-linear Multilayer Perceptron (MLP) structure. The Back Propagation (BP) algorithm is used to optimize the synaptic weights of the equalizer during the training mode. In the typical BP algorithm, the error signal is propagated from the output layer to the input layer while the learning rate parameter is held constant. In this study, the BP algorithm is modified so as to allow for the learning rate to be variable at each iteration and this achieves a faster convergence. The proposed algorithm is used to train the MLP based decision feedback equalizer (DFE) for time dispersive ISI channels. The equalizer is tested for a random input sequence of BPSK signals and its performance analysed in terms of the Bit Error Rates and speed of convergence. Simulation results show that the proposed algorithm improves the Bit Error Rate (BER) and rate of convergence.
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Usually, hearing impaired people use hearing aids which are implemented with speech enhancement
algorithms. Estimation of speech and estimation of nose are the components in single channel speech
enhancement system. The main objective of any speech enhancement algorithm is estimation of noise power
spectrum for non stationary environment. VAD (Voice Activity Detector) is used to identify speech pauses
and during these pauses only estimation of noise. MMSE (Minimum Mean Square Error) speech
enhancement algorithm did not enhance the intelligibility, quality and listener fatigues are the perceptual
aspects of speech. Novel evaluation approach SR (Signal to Residual spectrum ratio) based on uncertainty
parameter introduced for the benefits of hearing impaired people in non stationary environments to control
distortions. By estimation and updating of noise based on division of original pure signal into three parts
such as pure speech, quasi speech and non speech frames based on multiple threshold conditions. Different
values of SR and LLR demonstrate the amount of attenuation and amplification distortions. The proposed
method will compared with any one method WAT(Weighted Average Technique) Hence by using
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DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
Similar to Real Time Speech Enhancement in the Waveform Domain (20)
Investing in AI transformation today
The modern business advantage: Uncovering deep insights with AI
Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage.
AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive
operational efficiency, transform their products, and better serve their customers
Last year’s Global Risks Report warned of a world
that would not easily rebound from continued
shocks. As 2024 begins, the 19th edition of
the report is set against a backdrop of rapidly
accelerating technological change and economic
uncertainty, as the world is plagued by a duo of
dangerous crises: climate and conflict.
Underlying geopolitical tensions combined with the
eruption of active hostilities in multiple regions is
contributing to an unstable global order characterized
by polarizing narratives, eroding trust and insecurity.
At the same time, countries are grappling with the
impacts of record-breaking extreme weather, as
climate-change adaptation efforts and resources
fall short of the type, scale and intensity of climaterelated events already taking place. Cost-of-living
pressures continue to bite, amidst persistently
elevated inflation and interest rates and continued
economic uncertainty in much of the world.
Despondent headlines are borderless, shared
regularly and widely, and a sense of frustration at
the status quo is increasingly palpable. Together,
this leaves ample room for accelerating risks – like
misinformation and disinformation – to propagate
in societies that have already been politically and
economically weakened in recent years.
Just as natural ecosystems can be pushed to the
limit and become something fundamentally new;
such systemic shifts are also taking place across
other spheres: geostrategic, demographic and
technological. This year, we explore the rise of global
risks against the backdrop of these “structural
forces” as well as the tectonic clashes between
them. The next set of global conditions may not
necessarily be better or worse than the last, but the
transition will not be an easy one.
The report explores the global risk landscape in this
phase of transition and governance systems being
stretched beyond their limit. It analyses the most
severe perceived risks to economies and societies
over two and 10 years, in the context of these
influential forces. Could we catapult to a 3°C world
as the impacts of climate change intrinsically rewrite
the planet? Have we reached the peak of human
development for large parts of the global population,
given deteriorating debt and geo-economic
conditions? Could we face an explosion of criminality
and corruption that feeds on more fragile states and
more vulnerable populations? Will an “arms race” in
experimental technologies present existential threats
to humanity?
These transnational risks will become harder to
handle as global cooperation erodes. In this year’s
Global Risks Perception Survey, two-thirds of
respondents predict that a multipolar order will
dominate in the next 10 years, as middle and
great powers set and enforce – but also contest
- current rules and norms. The report considers
the implications of this fragmented world, where
preparedness for global risks is ever more critical but
is hindered by lack o
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce
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para convertirse en una enfermedad autÈntica, nosolÛgicamente bien defini-
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Este artÌculo revisa el conocimiento actual sobre el tema. En esta primera
parte se analizan su epidemiologÌa, patogenia y genÈtica; se enumeran los
temas prioritarios de investigaciÛn; se revisa su relaciÛn con el concepto de
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Artificial intelligence and machine learning capabilities are growing at an unprecedented rate. These technologies have many widely beneficial applications, ranging from machine translation to medical image analysis. Countless more such applications are being developed and can be expected over the long term. Less attention has historically been paid to the ways in which artificial intelligence can be used maliciously. This report surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats. We analyze, but do not conclusively resolve, the question of what the long-term equilibrium between attackers and defenders will be. We focus instead on what sorts of attacks we are likely to see soon if adequate defenses are not developed.
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals’ mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users’ level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metricspresentedintheliteratureandemploythesemetricsasinputtothenetwork. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.
La Hipertensión, es una de las mayores enfermedades que sufren los Hispanohablantes en el planeta . Es grato poder colocar este documento al público y haber podido hacer parte del equipo , ojalá sirvan a muchos las implementaciones. idioma más hablado según el foro Económico mundial - Me refiero al español ó castellano según sea -
segundo idioma y haber podido hacer parte de este equipo. Genuinamente, espero que se curen la mayor cantidad de personas con . Espero genuinamente puedan hacer algúna donación a este esfuerzo grupal. Espero Compartamos este "Paper" así como compartimos memes - En el sentido literal de la significancia-
** Refierase a Wikipedia sino tiene un diccionario a mano.
To thrive in the 21st century, students need more than traditional academic learning. They must be adept at collaboration, communication and problem-solving, which are some of the skills developed through social and emotional learning (SEL). Coupled with mastery of traditional skills, social and emotional proficiency will equip students to succeed in the swiftly evolving digital economy. In 2015, the World Economic Forum published a report that focused on the pressing issue of the 21st-century skills gap and ways to address it through technology (New Vision for Education: Unlocking the Potential of Technology). In that report, we defined a set of 16 crucial proficiencies for education in the 21st century. Those skills include six “foundational literacies”, such as literacy, numeracy and scientific literacy, and 10 skills that we labelled either “competencies” or “character qualities”. Competencies are the means by which students approach complex challenges; they include collaboration, communication and critical thinking and problem-solving. Character qualities are the ways in which students approach their changing environment; they include curiosity, adaptability and social and cultural awareness (see Exhibit 1).
In our current report, New Vision for Education: Fostering Social and Emotional Learning through Technology, we follow up on our 2015 report by exploring how these competencies and character qualities do more than simply deepen 21st-century skills. Together, they lie at the heart of SEL and are every bit as important as the foundational skills required for traditional academic learning. Although many stakeholders have defined SEL more narrowly, we believe the definition of SEL is evolving. We define SEL broadly to encompass the 10 competencies and character qualities.1 As is the case with traditional academic learning, technology can be invaluable at enabling SEL.
La expresión “futuro del trabajo” es actualmente uno de los conceptos más populares en las búsquedas en Google. Los numerosos avances tecnológicos de los últimos tiempos están modificando rápidamente la frontera entre las actividades realizadas por los seres humanos y las ejecutadas por las máquinas, lo cual está transformando el mundo del trabajo. Existe un creciente número de estudios e iniciativas que se están llevando a cabo con el objeto de analizar qué significan estos cambios en nuestro trabajo, en nuestros ingresos, en el futuro de nuestros hijos, en nuestras empresas y en nuestros gobiernos. Estos análisis se conducen principalmente desde la óptica de las economías avanzadas, y mucho menos desde la perspectiva de las economías en desarrollo y emergentes. Sin embargo, las diferencias en materia de difusión tecnológica, de estructuras económicas y demográficas, de niveles de educación y patrones
migratorios inciden de manera significativa en la manera en que estos cambios pueden afectar a los países en desarrollo y emergentes. Este estudio, El futuro del trabajo: perspectivas regionales, se centra en las repercusiones probables de estas tendencias en las economías en desarrollo y emergentes de África; Asia; Europa del Este, Asia Central y el Mediterráneo Sur y Oriental, y América Latina y el Caribe. Se trata de un esfuerzo mancomunado de los cuatro principales bancos regionales de desarrollo: el African Development Bank Group, el Asian Development Bank, el Banco Interamericano de Desarrollo y el European Bank for Reconstruction and Development. En el estudio se destacan las oportunidades que los cambios en la dinámica del trabajo podrían crear en nuestras regiones. El progreso tecnológico permitiría a los países con los que trabajamos crecer y alcanzar rápidamente mejores niveles de vida que en el pasado
Superada la Guerra Fría, el orden mundial dirigido por Estados Unidos se ve cuestionado por China y Rusia, dos potencias revisionistas que están acercando sus alineamientos estratégicos. China está en camino de convertirse en la mayor economía del mundo y en una potencia militar formidable a la que irrita la hegemonía de Estados Unidos. Parece que China, más que derrocar el orden mundial establecido, busca remodelarlo, especialmente en Asia, con la instauración de un orden sinocéntrico en el que todos los países del área asiática ponganlos intereses chinos por delante de los suyos propios. Está por ver si China tendrá las capacidades para conseguirlo, evitando el conflicto con Estados Unidos.
The increasing use of electronic forms of communication presents
new opportunities in the study of mental health, including the
ability to investigate the manifestations of psychiatric diseases un-
obtrusively and in the setting of patients’ daily lives. A pilot study to
explore the possible connections between bipolar affective disorder
and mobile phone usage was conducted. In this study, participants
were provided a mobile phone to use as their primary phone. This
phone was loaded with a custom keyboard that collected metadata
consisting of keypress entry time and accelerometer movement.
Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We pro-
pose an end-to-end deep architecture based on late fusion, named
DeepMood, to model the multi-view metadata for the prediction
of mood scores. Experimental results show that 90.31% prediction
accuracy on the depression score can be achieved based on session-
level mobile phone typing dynamics which is typically less than
one minute. It demonstrates the feasibility of using mobile phone
metadata to infer mood disturbance and severity
Defin
ing artificial intelligence is no easy matter. Since the mid
-
20th century when it
was first
recognized
as a specific field of research, AI has always been envisioned as
an evolving boundary, rather than a settled research field. Fundamentally, it refers
to
a programme whose ambitious objective is to understand and reproduce human
cognition; creating cognitive processes comparable to those found in human beings.
Therefore, we are naturally dealing with a wide scope here, both in terms of the
technical proced
ures that can be employed and the various disciplines that can be
called upon: mathematics, information technology, cognitive sciences, etc. There is
a great variety of approaches when it comes to AI: ontological, reinforcement
learning, adversarial learni
ng and neural networks, to name just a few. Most of them
have been known for decades and many of the algorithms used today were
developed in the ’60s and ’70s.
Since the 1956 Dartmouth conference, artificial intelligence has alternated between
periods of
great enthusiasm and disillusionment, impressive progress and frustrating
failures. Yet, it has relentlessly pushed back the limits of what was only thought to
be achievable by human beings. Along the way, AI research has achieved significant
successes: o
utperforming human beings in complex games (chess, Go),
understanding natural language, etc. It has also played a critical role in the history
of mathematics and information technology. Consider how many softwares that we
now take for granted once represen
ted a major breakthrough in AI: chess game
apps, online translation programmes, etc
Vast
amounts
of
data, faster processing
power,
and
increas
-
ingly smarter algorithms are powering artificial intelligence
(AI) applications and associated use cases across
consumer,
finance, healthcare, manufacturing, transportation & logistics,
and government sectors around the world
-
enabling smarter
&
intelligent applications to speak, listen, and make decisions
in unprecedented ways. As AI technologies and deployments
sweep through virtually
every
industry, a wide range
of
use
cases are beginning to illustrate the potential business
oppor
-
tunities, a
nd inspire changes to existing business processes
leading to newer business
models.
In this paper, we propose an Attentional Generative Ad-
versarial Network (AttnGAN) that allows attention-driven,
multi-stage refinement for fine-grained text-to-image gener-
ation. With a novel attentional generative network, the At-
tnGAN can synthesize fine-grained details at different sub-
regions of the image by paying attentions to the relevant
words in the natural language description. In addition, a
deep attentional multimodal similarity model is proposed to
compute a fine-grained image-text matching loss for train-
ing the generator. The proposed AttnGAN significantly out-
performs the previous state of the art, boosting the best re-
ported inception score by 14.14% on the CUB dataset and
170.25% on the more challenging COCO dataset. A de-
tailed analysis is also performed by visualizing the atten-
tion layers of the AttnGAN. It for the first time shows that
the layered attentional GAN is able to automatically select
the condition at the word level for generating different parts
of the image
The Hamilton Project • Brookings i
Seven Facts on Noncognitive Skills
from Education to the Labor Market
Introduction
Cognitive skills—that is, math and reading skills that are measured by standardized tests—are generally
understood to be of critical importance in the labor market. Most people find it intuitive and indeed
unsurprising that cognitive skills, as measured by standardized tests, are important for students’ later-life
outcomes. For example, earnings tend to be higher for those with higher levels of cognitive skills. What is
less well understood—and is the focus of these economic facts—is that noncognitive skills are also integral to
educational performance and labor-market outcomes.
Due in large part to research pioneered in economics by Nobel laureate James J. Heckman, there is a robust and
growing body of evidence that noncognitive skills function similarly to cognitive skills, strongly improving
labor-market outcomes. These noncognitive skills—often referred to in the economics literature as soft skills and
elsewhere as social, emotional, and behavioral skills—include qualities like perseverance, conscientiousness,
and self-control, as well as social skills and leadership ability (Duckworth and Yeager 2015). The value of these
qualities in the labor market has increased over time as the mix of jobs has shifted toward positions requiring
noncognitive skills. Evidence suggests that the labor-market payoffs to noncognitive skills have been increasing
over time and the payoffs are particularly strong for individuals who possess both cognitive and noncognitive
skills (Deming 2015; Weinberger 2014).
Although we draw a conceptual distinction between noncognitive skills and cognitive skills, it is not possible to
disentangle these concepts fully. All noncognitive skills involve cognition, and some portion of performance on
cognitive tasks is made possible by noncognitive skills. For the purposes of this document, the term “cognitive
skills” encompasses intelligence; the ability to process, learn, think, and reason; and substantive knowledge
as reflected in indicators of academic achievement. Since the No Child Left Behind Act of 2001, education
policy has focused on accountability policies aimed at improving cognitive skills and closing test score gaps
across groups. These policies have been largely successful, particularly for math achievement (Dee and Jacob
2011; Wong, Cook, and Steiner 2009) and among students most exposed to accountability pressure (Neal and
Schanzenbach 2010). What has received less attention in policy debates is the importance of noncognitive skills.
Despite significant recent advances in the field of face
recognition [10, 14, 15, 17], implementing face verification
and recognition efficiently at scale presents serious chal-
lenges to current approaches. In this paper we present a
system, called FaceNet, that directly learns a mapping from
face images to a compact Euclidean space where distances
directly correspond to a measure of face similarity. Once
this space has been produced, tasks such as face recogni-
tion, verification and clustering can be easily implemented
using standard techniques with FaceNet embeddings as fea-
ture vectors.
Our method uses a deep convolutional network trained
to directly optimize the embedding itself, rather than an in-
termediate bottleneck layer as in previous deep learning
approaches. To train, we use triplets of roughly aligned
matching / non-matching face patches generated using a
novel online triplet mining method. The benefit of our
approach is much greater representational efficiency: we
achieve state-of-the-art face recognition performance using
only 128-bytes per face.
On the widely used Labeled Faces in the Wild (LFW)
dataset, our system achieves a new record accuracy of
99.63%
. On YouTube Faces DB it achieves
95.12%
. Our
system cuts the error rate in comparison to the best pub-
lished result [15] by 30% on both datasets.
Forklift Classes Overview by Intella PartsIntella Parts
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NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Final project report on grocery store management system..pdf
Real Time Speech Enhancement in the Waveform Domain
1. arXiv:2006.12847v3
[eess.AS]
6
Sep
2020
Real Time Speech Enhancement in the Waveform Domain
Alexandre Défossez1,2,3
, Gabriel Synnaeve1
, Yossi Adi1
1
Facebook AI Research 2
INRIA 3
PSL Research University
defossez,gab,adiyoss@fb.com
Abstract
We present a causal speech enhancement model working on the
raw waveform that runs in real-time on a laptop CPU. The pro-
posed model is based on an encoder-decoder architecture with
skip-connections. It is optimized on both time and frequency
domains, using multiple loss functions. Empirical evidence
shows that it is capable of removing various kinds of back-
ground noise including stationary and non-stationary noises,
as well as room reverb. Additionally, we suggest a set of
data augmentation techniques applied directly on the raw wave-
form which further improve model performance and its gener-
alization abilities. We perform evaluations on several standard
benchmarks, both using objective metrics and human judge-
ments. The proposed model matches state-of-the-art perfor-
mance of both causal and non causal methods while working
directly on the raw waveform.
Index Terms: Speech enhancement, speech denoising, neural
networks, raw waveform
1. Introduction
Speech enhancement is the task of maximizing the perceptual
quality of speech signals, in particular by removing background
noise. Most recorded conversational speech signal contains
some form of noise that hinders intelligibility, such as street
noise, dogs barking, keyboard typing, etc. Thus, speech en-
hancement is a particularly important task in itself for audio
and video calls [1], hearing aids [2], and can also help auto-
matic speech recognition (ASR) systems [3]. For many such
applications, a key feature of a speech enhancement system is
to run in real time and with as little lag as possible (online), on
the communication device, preferably on commodity hardware.
Decades of work in speech enhancement showed feasible
solutions which estimated the noise model and used it to re-
cover noise-deducted speech [4, 5]. Although those approaches
can generalize well across domains they still have trouble deal-
ing with commons noises such as non-stationary noise or a bab-
ble noise which is encountered when a crowd of people are
simultaneously talking. The presence of such noise types de-
grades hearing intelligibility of human speech greatly [6]. Re-
cently, deep neural networks (DNN) based models perform sig-
nificantly better on non-stationary noise and babble noise while
generating higher quality speech in objective and subjective
evaluations over traditional methods [7, 8]. Additionally, deep
learning based methods have also shown to be superior over tra-
ditional methods for the related task of a single-channel source
separation [9, 10, 11].
Inspired by these recent advances, we proposed a real-time
version of the DEMUCS [11] architecture adapted for speech
enhancement. It consists of a causal model, based on convolu-
tions and LSTMs, with a frame size of 40ms, a stride of 16ms,
and that runs faster than real-time on a single laptop CPU core.
For audio quality purposes, our model goes from waveform to
waveform, through hierarchical generation (using U-Net [12]
like skip-connections). We optimize the model to directly out-
put the “clean” version of the speech signal while minimizing a
regression loss function (L1 loss), complemented with a spec-
trogram domain loss [13, 14]. Moreover, we proposed a set
of simple and effective data augmentation techniques: namely
frequency band masking and signal reverberation. Although
enforcing a vital real-time constraint on model run-time, our
model yields comparable performance to state of the art model
by objective and subjective measures.
Although, multiple metrics exist to measure speech en-
hancement systems these have shown to not correlate well with
human judgements [1]. Hence, we report results for both objec-
tive metrics as well as human evaluation. Additionally we con-
duct an ablation study over the loss and augmentation functions
to better highlight the contribution of each part. Finally, we ana-
lyzed the artifacts of the enhancement process using Word Error
Rates (WERs) produced by an Automatic Speech Recognition
(ASR) model.
Results suggest that the proposed method is comparable to
the current state-of-the-art model across all metrics while work-
ing directly on the raw waveform. Moreover, the enhanced sam-
ples are found to be beneficial for improving an ASR model
under noisy conditions.
2. Model
2.1. Notations and problem settings
We focus on monaural (single-microphone) speech enhance-
ment that can operate in real-time applications. Specifically,
given an audio signal x ∈ RT
, composed of a clean speech
y ∈ RT
that is corrupted by an additive background signal
n ∈ RT
so that x = y + n. The length, T, is not a fixed
value across samples, since the input utterances can have dif-
ferent durations. Our goal is to find an enhancement function f
such that f(x) ≈ y.
In this study we set f to be the DEMUCS architecture [11],
which was initially developed for music source separation, and
adapt it to the task of causal speech enhancement, a visual de-
scription of the model can be seen in Figure 1a.
2.2. DEMUCS architecture
DEMUCS consists in a multi-layer convolutional encoder and
decoder with U-net [12] skip connections, and a sequence mod-
eling network applied on the encoders’ output. It is character-
ized by its number of layers L, initial number of hidden chan-
nels H, layer kernel size K and stride S and resampling factor
U. The encoder and decoder layers are numbered from 1 to L
(in reverse order for the decoder, so layers at the same scale have
the same index). As we focus on monophonic speech enhance-
ment, the input and output of the model has a single channel
only.
Formally, the encoder network E gets as input the raw wave
form and outputs a latent representation E(x) = z. Each layer
2. Encoder1(Cin = 1, Cout = H)
Encoder2(Cin = H, Cout = 2H)
. . .
EncoderL(Cin = 2L−2
H, Cout = 2L−1
H)
L S T M
hidden size=2L−1
H
2 layers
DecoderL(Cin = 2L−1
H, Cout = 2L−2
H)
. . .
Decoder2(Cin = 2H, Cout = H)
Decoder1(Cin = H, Cout = 1)
(a) Causal DEMUCS with the noisy speech as input on the bottom
and the clean speech as output on the top. Arrows represents U-
Net skip connections. H controls the number of channels in the
model and L its depth.
GLU(Conv1d(Cin, 2Cin, K = 1, S = 1))
ConvTr1d(Cin, Cout, K, S)
Encoderi
+
Decoderi+1 or LSTM
Output or ReLU then Decoderi−1
Relu(Conv1d(Cin, Cout, K, S))
GLU(Conv1d(Cout, 2Cout, K = 1, S = 1))
Decoderi
Input or Encoderi−1
Encoderi+1 or LSTM
(b) View of each encoder (bottom) and decoder layer (top). Arrows
are connections to other parts of the model. Cin (resp. Cout) is
the number of input channels (resp. output), K the kernel size and
S the stride.
Figure 1: Causal DEMUCS architecture on the left, with detailed representation of the encoder and decoder layers on the right. The on
the fly resampling of the input/output by a factor of U is not represented.
consists in a convolution layer with a kernel size of K and stride
of S with 2i−1
H output channels, followed by a ReLU activa-
tion, a “1x1” convolution with 2i
H output channels and finally
a GLU [15] activation that converts back the number of chan-
nels to 2i−1
H, see Figure 1b for a visual description.
Next, a sequence modeling R network takes the latent rep-
resentation z as input and outputs a non-linear transformation
of the same size, R(z) = LST M(z) + z, denoted as ẑ. The
LSTM network consists of 2-layers and 2L−1
H hidden units.
For causal prediction, we use an unidirectional LSTM, while
for non causal models, we use a bidirectional LSTM, followed
by a linear layer to merge the both outputs.
Lastly, a decoder network D, takes as input ẑ and outputs
an estimation of clean signal D(ẑ) = ŷ. The i-th layer of
the decoder takes as input 2i−1
H channels, and applies a 1x1
convolution with 2i
H channels, followed by a GLU activation
function that outputs 2i−1
H channels and finally a transposed
convolution with a kernel size of 8, stride of 4, and 2i−2
H out-
put channels accompanied by a ReLU function. For the last
layer the output is a single channel and has no ReLU. A skip
connection connects the output of the i-th layer of the encoder
and the input of the i-th layer of the decoder, see Figure 1a.
We initialize all model parameters using the scheme pro-
posed by [16]. Finally, we noticed that upsampling the audio by
a factor U before feeding it to the encoder improves accuracy.
We downsample the output of the model by the same amount.
The resampling is done using a sinc interpolation filter [17], as
part of the end-to-end training, rather than a pre-processing step.
2.3. Objective
We use the L1 loss over the waveform together with a multi-
resolution STFT loss over the spectrogram magnitudes simi-
larly to the one proposed in [13, 14]. Formally, given y and
ŷ be the clean signal and the enhanced signal respectively. We
define the STFT loss to be the sum of the spectral convergence
(sc) loss and the magnitude loss as follows,
Lstft(y, ŷ) = Lsc(y, ŷ) + Lmag(y, ŷ)
Lsc(y, ŷ) =
k|STFT(y)| − |STFT(ŷ)|kF
k|STFT(y)|kF
Lmag(y, ŷ) =
1
T
k log |STFT(y)| − log |STFT(ŷ)|k1
(1)
where k · kF and k · k1 are the Frobenius the L1 norms re-
spectively. We define the multi-resolution STFT loss to be the
sum of all STFT loss functions using different STFT parame-
ters. Overall we wish to minimize the following,
1
T
[ky − ŷk1 +
M
X
i=1
L
(i)
stft (y, ŷ)] (2)
where M is the number of STFT losses, and each L
(i)
stft applies
the STFT loss at different resolution with number of FFT bins
∈ {512, 1024, 2048}, hop sizes ∈ {50, 120, 240}, and lastly
window lengths ∈ {240, 600, 1200}.
3. Experiments
We performed several experiments to evaluate the proposed
method against several highly competitive models. We report
objective and subjective measures on the Valentini et al. [18]
and Deep Noise Suppression (DNS) [19] benchmarks. More-
over, we run an ablation study over the augmentation and loss
functions. Finally, we assessed the usability of the enhanced
samples to improve ASR performance under noisy condi-
tions. Code and samples can be found in the following link:
https://github.com/facebookresearch/denoiser.
3.1. Implementation details
Evaluation Methods We evaluate the quality of the enhanced
speech using both objective and subjective measures. For the
objective measures we use: (i) PESQ: Perceptual evaluation
of speech quality, using the wide-band version recommended
in ITU-T P.862.2 [24] (from 0.5 to 4.5) (ii) Short-Time Objec-
tive Intelligibility (STOI) [25] (from 0 to 100) (iii) CSIG: Mean
opinion score (MOS) prediction of the signal distortion attend-
ing only to the speech signal [26] (from 1 to 5). (iv) CBAK:
MOS prediction of the intrusiveness of background noise [26]
(from 1 to 5). (v) COVL: MOS prediction of the overall ef-
fect [26] (from 1 to 5).
For the subjective measure, we conducted a MOS study as
recommended in ITU-T P.835 [27]. For that, we launched a
crowd source evaluation using the CrowdMOS package [28].
We randomly sample 100 utterances and each one was scored
3. Table 1: Objective and Subjective measures of the proposed method against SOTA models using the Valentini benchmark [18].
PESQ STOI (%) pred. pred. pred. MOS MOS MOS Causal
CSIG CBAK COVL SIG BAK OVL
Noisy 1.97 91.5 3.35 2.44 2.63 4.08 3.29 3.48 -
SEGAN [7] 2.16 - 3.48 2.94 2.80 - - - No
Wave U-Net [20] 2.40 - 3.52 3.24 2.96 - - - No
SEGAN-D [8] 2.39 - 3.46 3.11 3.50 - - - No
MMSE-GAN [21] 2.53 93 3.80 3.12 3.14 - - - No
MetricGAN [22] 2.86 - 3.99 3.18 3.42 - - - No
DeepMMSE [23] 2.95 94 4.28 3.46 3.64 - - - No
DEMUCS (H=64, S=2 ,U=2) 3.07 95 4.31 3.4 3.63 4.02 3.55 3.63 No
Wiener 2.22 93 3.23 2.68 2.67 - - - Yes
DeepMMSE [23] 2.77 93 4.14 3.32 3.46 4.11 3.69 3.67 Yes
DEMUCS (H=48,S=4, U=4) 2.93 95 4.22 3.25 3.52 4.08 3.59 3.40 Yes
DEMUCS (H=64,S=4, U=4) 2.91 95 4.20 3.26 3.51 4.03 3.69 3.39 Yes
DEMUCS (H=64,S=4, U=4) + dry=0.05 2.88 95 4.14 3.21 3.54 4.10 3.58 3.72 Yes
DEMUCS (H=64,S=4, U=4) + dry=0.1 2.81 95 4.07 3.10 3.42 4.18 3.45 3.60 Yes
by 15 different raters along three axis: level of distortion, in-
trusiveness of background noise, and overall quality. Averaging
results across all annotators and queries gives the final scores.
Training We train the DEMUCS model for 400 epochs on the
Valentini [18] dataset and 250 on the DNS [19] dataset. We
use the L1 loss between the predicted and ground truth clean
speech waveforms, and for the Valentini dataset, also add the
STFT loss described in Section 2.3 with a weight of 0.5. We
use the Adam optimizer with a step size of 3e−4, a momentum
of β1 = 0.9 and a denominator momentum β2 = 0.999. For the
Valentini dataset, we use the original validation set and keep the
best model, for the DNS dataset we train without a validation set
and keep the last model. The audio is sampled at 16 kHz.
Model We use three variants of the DEMUCS architecture de-
scribed in Section 2. For the non causal DEMUCS , we take
U=2, S=2, K=8, L=5 and H=64. For the causal DEMUCS ,
we take U=4, S=4, K=8 and L=5, and either H=48, or
H=64. We normalize the input by its standard deviation be-
fore feeding it to the model and scale back the output by the
same factor. For the evaluation of causal models, we use an
online estimate of the standard deviation. With this setup, the
causal DEMUCS processes audio has a frame size of 37 ms and
a stride of 16 ms.
Data augmentation We always apply a random shift be-
tween 0 and S seconds. The Remix augmentation shuffles the
noises within one batch to form new noisy mixtures. Band-
Mask is a band-stop filter with a stop band between f0 and
f1, sampled to remove 20% of the frequencies uniformly in the
mel scale. This is equivalent, in the waveform domain, to the
SpecAug augmentation [29] used for ASR training. Revecho:
given an initial gain λ, early delay τ and RT60, it adds to the
noisy signal a series of N decaying echos of the clean speech
and noise. The n-th echo has a delay of nτ + jitter and a gain
of ρn
λ. N and ρ are chosen so that when the total delay reaches
RT60, we have ρN
≤ 1e−3. λ, τ and RT60 are sampled uni-
formly respectively over [0, 0.3], [10, 30] ms, [0.3, 1.3] sec.
We use the random shift for all datasets, Remix and Ban-
mask for Valentini [18], and Revecho only for DNS [19].
Causal streaming evaluation In order to test our causal
model in real conditions we use a specific streaming implemen-
tation at test time. Instead of normalizing by the standard de-
viation of the audio, we use the standard deviation up to the
current position (i.e. we use the cumulative standard deviation).
Table 2: Subjective measures of the proposed method with
different treatment of reverb, on the DNS blind test set [19].
Recordings are divided in 3 categories: no reverb, reverb (arti-
ficial) and real recordings. We report the OVL MOS. All models
are causal. For DEMUCS , we take U=4, H=64 and S=4.
No Rev. Reverb Real Rec.
Noisy 3.1 3.2 2.6
NS-Net [30, 19] 3.2 3.1 2.8
DEMUCS , no reverb 3.7 2.7 3.3
DEMUCS , remove reverb 3.6 2.6 3.2
DEMUCS , keep reverb 3.6 3.0 3.2
DEMUCS , keep 10% rev. 3.3 3.3 3.1
DEMUCS , keep 10% rev., two sources 3.6 2.8 3.5
We keep a small buffer of past input/output to limit the side ef-
fect of the sinc resampling filters. For the input upsampling, we
also use a 3ms lookahead, which takes the total frame size of
the model to 40 ms. When applying the model to a given frame
of the signal, the rightmost part of the output is invalid, because
future audio is required to compute properly the output of the
transposed convolutions. Nonetheless, we noticed that using
this invalid part as a padding for the streaming downsampling
greatly improves the PESQ. The streaming implementation is
pure PyTorch. Due to the overlap between frames, care was
taken to cache the output of the different layers as needed.
3.2. Results
Table 1 summarizes the results for Valentini [18] dataset us-
ing both causal and non-causal models. Results suggest that
DEMUCS matched current SOTA model (DeepMMSE [23]) us-
ing both objective and subjective measures, while working di-
rectly on the raw waveform and without using extra training
data. Additionally, DEMUCS is superior to the other baselines
methods, (may they be causal or non-causal), by a significant
margin. We also introduce a dry/wet knob, i.e. we output
dry · x + (1 − dry) · ŷ, which allows to control the trade-off
between noise removal and conservation of the signal. We no-
tice that a small amount of bleeding (5%) improves the overall
perceived quality.
We present on Table 2 the overall MOS evaluations on the
3 categories of the DNS [19] blind test set: no reverb (synthetic
mixture without reverb), reverb (synthetic mixture, with artifi-
cial reverb) and real recordings. We test different strategies for
4. Table 3: Ablation study for the causal DEMUCS model with
H=64, S=4, U=4 using the Valentini benchmark [18].
PESQ STOI (%)
Reference 2.91 95
no BandMask (BM) 2.87 95
no BM, no remix 2.86 95
no BM, no remix, no STFT loss 2.68 94
no BM, no remix, no STFT loss , no shift 2.38 93
the reverb-like Revecho augmentation described in Section 3.1.
We either ask the model to remove it (dereverberation), keep it,
or keep only part of it. Finally, we either add the same reverb to
the speech and noise or use different jitters to simulate having
two distinct sources. We entered the challenge with the “remove
reverb” model, with poor performance on the Reverb category
due to dereverberation artifacts1
. Doing partial dereverberation
improves the overall rating, but not for real recordings (which
have typically less reverb already). On real recordings, simulat-
ing reverb with two sources improves the ratings.
3.3. Ablation
In order to better understand the influence of different compo-
nents in the proposed model on the overall performance, we
conducted an ablation study over the augmentation functions
and loss functions. We use the causal DEMUCS version and
report PESQ and STOI for each of the methods. Results are
presented in Table 3. Results suggest that each of the compo-
nents contribute to overall performance, with the STFT loss and
time shift augmentation producing the biggest increase in per-
formance. Notice, surprisingly the remix augmentation function
has a minor contribution to the overall performance.
3.4. Real-Time Evaluation
We computed the Real-Time Factor (RTF, e.g. time to enhance
a frame divided by the stride) under the streaming setting to
better match real-world conditions. We benchmark this imple-
mentation on a quad-core Intel i5 CPU (2.0 GHz, up to AVX2
instruction set). The RTF is 1.05 for the H=64 version, while
for the H=48 the RTF is 0.6. When restricting execution to a
single core, the H=48 model still achieves a RTF of 0.8, mak-
ing it realistic to use in real conditions, for instance along a
video call software. We do not provide RTF results for Deep-
MMSE [23] since no streaming implementation was provided
by the authors, thus making it an unfair comparison.
3.5. The effect on ASR models
Lastly, we evaluated the usability of the enhanced samples to
improve ASR performance under noisy conditions. To that
end, we synthetically generated noisy data using the LIB-
RISPEECH dataset [31] together with noises from the test set of
the DNS [19] benchmark. We created noisy samples in a con-
trolled setting where we mixed the clean and noise files with
SNR levels ∈ {0, 10, 20, 30}. For the ASR model we used a
Convolutions and Transformer based acoustic model which get
states-of-the-art results on LIBRISPEECH, as described in [32].
To get Word Error Rates (WERs) we follow a simple Viterbi
(argmax) decoding with neither language model decoding nor
beam-search. That way, we can better understand the impact of
1MOS for the baselines differ from the challenge as we evaluated on
100 examples per category, and used ITU-T P.835 instead of P.808.
Table 4: ASR results with a state-of-the-art acoustic model,
Word Error Rates without decoding, no language model. Re-
sults on the LIBRISPEECH validation sets with added noise from
the test set of DNS, and enhanced by DEMUCS .
Viterbi WER on dev-clean enhanced dev-other enhanced
original (no noise) 2.1 2.2 4.6 4.7
noisy SNR 0 12.0 6.9 21.1 14.7
noisy SNR 10 9.8 6.3 18.4 13.1
noisy SNR 20 5.2 4.0 11.7 9.4
noisy SNR 30 3.3 2.9 7.6 7.2
the enhanced samples on the acoustic model. Results are de-
picted in Table 4. DEMUCS is able to recover up to 51% of
the WER lost to the added noise at SNR 0, and recovering in
average 41% of the WER on dev-clean and 31% on dev-other.
As the acoustic model was not retrained on denoised data, those
results show the direct applicability of speech enhancement to
ASR systems as a black-box audio preprocessing step.
4. Related Work
Traditionally speech enhancement methods generate either an
enhanced version of the magnitude spectrum or produce an esti-
mate of the ideal binary mask (IBM) that is then used to enhance
the magnitude spectrum [5, 33].
Over the last years, there has been a growing interest to-
wards DNN based methods for speech enhancement [34, 35,
36, 37, 20, 38, 39, 22, 40, 7, 41, 8, 42, 21, 37]. In [34] a deep
feed-forward neural network was used to generate a frequency-
domain binary mask using a cost function in the waveform do-
main. Authors in [43] suggested to use a multi-objective loss
function to further improve speech quality. Alternatively au-
thors in [44, 35] use a recursive neural network (RNN) for
speech enhancement. In [7] the authors proposed an end-to-end
method , namely Speech Enhancement Generative Adversar-
ial Networks (SEGAN) to perform enhancement directly from
the raw waveform. The authors in [41, 8, 42, 21] further im-
prove such optimization. In [37] the authors suggest to use a
WaveNet [45] model to perform speech denoising by learning a
function to map noisy to clean signals.
While considering causal methods, the authors in [46] pro-
pose a convolutional recurrent network at the spectral level for
real-time speech enhancement, while Xia, Yangyang, et al. [30]
suggest to remove the convolutional layers and apply a weighted
loss function to further improve results in the real-time setup.
Recently, the authors in [23] provide impressive results for both
causal and non-causal models using a minimum mean-square
error noise power spectral density tracker, which employs a
temporal convolutional network (TCN) a priori SNR estimator.
5. Discussion
We have showed how DEMUCS , a state-of-the-art architecture
developed for music source separation in the waveform domain,
could be turned into a causal speech enhancer, processing audio
in real time on consumer level CPU. We tested DEMUCS on the
standard Valentini benchmark and achieved state-of-the-art re-
sult without using extra training data. We also test our model
in real reverberant conditions with the DNS dataset. We empir-
ically demonstrated how augmentation techniques (reverb with
two sources, partial dereverberation) can produce a significant
improvement in subjective evaluations. Finally, we showed that
our model can improve the performance of an ASR model in
noisy conditions even without retraining of the model.
5. 6. References
[1] C. K. Reddy et al., “A scalable noisy speech dataset and online
subjective test framework,” preprint arXiv:1909.08050, 2019.
[2] C. K. A. Reddy et al., “An individualized super-gaussian sin-
gle microphone speech enhancement for hearing aid users with
smartphone as an assistive device,” IEEE signal processing let-
ters, vol. 24, no. 11, pp. 1601–1605, 2017.
[3] C. Zorila, C. Boeddeker, R. Doddipatla, and R. Haeb-Umbach,
“An investigation into the effectiveness of enhancement in asr
training and test for chime-5 dinner party transcription,” preprint
arXiv:1909.12208, 2019.
[4] J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth
compression of noisy speech,” Proceedings of the IEEE, vol. 67,
no. 12, pp. 1586–1604, 1979.
[5] Y. Ephraim and D. Malah, “Speech enhancement using a
minimum-mean square error short-time spectral amplitude esti-
mator,” IEEE Transactions on acoustics, speech, and signal pro-
cessing, vol. 32, no. 6, pp. 1109–1121, 1984.
[6] N. Krishnamurthy and J. H. Hansen, “Babble noise: modeling,
analysis, and applications,” IEEE transactions on audio, speech,
and language processing, vol. 17, no. 7, pp. 1394–1407, 2009.
[7] S. Pascual, A. Bonafonte, and J. Serra, “Segan: Speech enhance-
ment generative adversarial network,” preprint arXiv:1703.09452,
2017.
[8] H. Phan et al., “Improving gans for speech enhancement,”
preprint arXiv:2001.05532, 2020.
[9] Y. Luo and N. Mesgarani, “Conv-TASnet: Surpassing ideal time–
frequency magnitude masking for speech separation,” IEEE/ACM
transactions on audio, speech, and language processing, vol. 27,
no. 8, pp. 1256–1266, 2019.
[10] E. Nachmani, Y. Adi, and L. Wolf, “Voice separation with an un-
known number of multiple speakers,” arXiv:2003.01531, 2020.
[11] A. Dfossez et al., “Music source separation in the waveform do-
main,” 2019, preprint arXiv:1911.13254.
[12] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional
networks for biomedical image segmentation,” in International
Conference on Medical image computing and computer-assisted
intervention, 2015.
[13] R. Yamamoto, E. Song, and J.-M. Kim, “Parallel wavegan:
A fast waveform generation model based on generative ad-
versarial networks with multi-resolution spectrogram,” preprint
arXiv:1910.11480, 2019.
[14] ——, “Probability density distillation with generative adversarial
networks for high-quality parallel waveform generation,” preprint
arXiv:1904.04472, 2019.
[15] Y. N. Dauphin et al., “Language modeling with gated convolu-
tional networks,” in ICML, 2017.
[16] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification,”
in ICCV, 2015.
[17] J. Smith and P. Gossett, “A flexible sampling-rate conversion
method,” in ICASSP, vol. 9. IEEE, 1984, pp. 112–115.
[18] C. Valentini-Botinhao, “Noisy speech database for training speech
enhancement algorithms and tts models,” 2017.
[19] C. K. A. Reddy et al., “The interspeech 2020 deep noise sup-
pression challenge: Datasets, subjective speech quality and test-
ing framework,” 2020.
[20] C. Macartney and T. Weyde, “Improved speech enhancement with
the wave-u-net,” preprint arXiv:1811.11307, 2018.
[21] M. H. Soni, N. Shah, and H. A. Patil, “Time-frequency masking-
based speech enhancement using generative adversarial network,”
in ICASSP. IEEE, 2018, pp. 5039–5043.
[22] S.-W. Fu et al., “Metricgan: Generative adversarial networks
based black-box metric scores optimization for speech enhance-
ment,” in ICML, 2019.
[23] Q. Zhang et al., “Deepmmse: A deep learning approach to mmse-
based noise power spectral density estimation,” IEEE/ACM Trans-
actions on Audio, Speech, and Language Processing, 2020.
[24] I.-T. Recommendation, “Perceptual evaluation of speech quality
(pesq): An objective method for end-to-end speech quality as-
sessment of narrow-band telephone networks and speech codecs,”
Rec. ITU-T P. 862, 2001.
[25] C. H. Taal et al., “An algorithm for intelligibility prediction of
time–frequency weighted noisy speech,” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 19, no. 7, pp.
2125–2136, 2011.
[26] Y. Hu and P. C. Loizou, “Evaluation of objective quality measures
for speech enhancement,” IEEE Transactions on audio, speech,
and language processing, vol. 16, no. 1, pp. 229–238, 2007.
[27] I. Recommendation, “Subjective test methodology for evaluating
speech communication systems that include noise suppression al-
gorithm,” ITU-T recommendation, p. 835, 2003.
[28] F. Protasio Ribeiro et al., “Crowdmos: An approach for crowd-
sourcing mean opinion score studies,” in ICASSP. IEEE, 2011.
[29] D. S. Park et al., “Specaugment: A simple data augmentation
method for automatic speech recognition,” in Interspeech, 2019.
[30] Y. Xia et al., “Weighted speech distortion losses for neural-
network-based real-time speech enhancement,” preprint
arXiv:2001.10601, 2020.
[31] V. Panayotov et al., “Librispeech: an asr corpus based on public
domain audio books,” in ICASSP. IEEE, 2015, pp. 5206–5210.
[32] G. Synnaeve et al., “End-to-end asr: from supervised to
semi-supervised learning with modern architectures,” preprint
arXiv:1911.08460, 2019.
[33] Y. Hu and P. C. Loizou, “Subjective comparison of speech en-
hancement algorithms,” in ICASSP, vol. 1. IEEE, 2006, pp. I–I.
[34] Y. Wang and D. Wang, “A deep neural network for time-domain
signal reconstruction,” in ICASSP. IEEE, 2015, pp. 4390–4394.
[35] F. Weninger, H. Erdogan, S. Watanabe, E. Vincent, J. Le Roux,
J. R. Hershey, and B. Schuller, “Speech enhancement with lstm
recurrent neural networks and its application to noise-robust asr,”
in International Conference on Latent Variable Analysis and Sig-
nal Separation. Springer, 2015, pp. 91–99.
[36] Y. Xu et al., “A regression approach to speech enhancement based
on deep neural networks,” IEEE/ACM Transactions on Audio,
Speech, and Language Processing, vol. 23, no. 1, pp. 7–19, 2014.
[37] D. Rethage, J. Pons, and X. Serra, “A wavenet for speech denois-
ing,” in ICASSP. IEEE, 2018, pp. 5069–5073.
[38] A. Nicolson and K. K. Paliwal, “Deep learning for minimum
mean-square error approaches to speech enhancement,” Speech
Communication, vol. 111, pp. 44–55, 2019.
[39] F. G. Germain, Q. Chen, and V. Koltun, “Speech denoising with
deep feature losses,” preprint arXiv:1806.10522, 2018.
[40] M. Nikzad, A. Nicolson, Y. Gao, J. Zhou, K. K. Paliwal, and
F. Shang, “Deep residual-dense lattice network for speech en-
hancement,” preprint arXiv:2002.12794, 2020.
[41] K. Wang, J. Zhang, S. Sun, Y. Wang, F. Xiang, and L. Xie, “In-
vestigating generative adversarial networks based speech derever-
beration for robust speech recognition,” arXiv:1803.10132, 2018.
[42] D. Baby and S. Verhulst, “Sergan: Speech enhancement using
relativistic generative adversarial networks with gradient penalty,”
in ICASSP. IEEE, 2019, pp. 106–110.
[43] Y. Xu et al., “Multi-objective learning and mask-based post-
processing for deep neural network based speech enhancement,”
preprint arXiv:1703.07172, 2017.
[44] F. Weninger et al., “Discriminatively trained recurrent neural
networks for single-channel speech separation,” in GlobalSIP.
IEEE, 2014, pp. 577–581.
[45] A. v. d. Oord et al., “Wavenet: A generative model for raw audio,”
preprint arXiv:1609.03499, 2016.
[46] K. Tan and D. Wang, “A convolutional recurrent neural network
for real-time speech enhancement.” in Interspeech, vol. 2018,
2018, pp. 3229–3233.