The document summarizes an academic paper on audio inpainting using generative adversarial networks (GANs). It discusses using a dual discriminator Wasserstein GAN (D2WGAN) model to fill in missing audio content of 500-550ms by extracting short and long-range borders. The model was tested on piano, guitar, and piano+orchestra datasets, with the guitar dataset achieving the best results. Increasing the training steps significantly improved performance without overfitting. Future work could explore different border lengths, additional network layers, and combining waveforms and spectrograms.
This document summarizes a research paper on efficient Transformer-based speech enhancement using long frames and STFT magnitudes. It introduces the task of speech enhancement and issues with learned-domain approaches. The proposed method uses STFT magnitudes as input to an encoder-decoder architecture with a Transformer masker. Experiments show the method achieves equivalent quality and intelligibility scores compared to learned features, while reducing computations by 8x for 10-second utterances. The conclusions are that using STFT magnitudes enables efficient, high-quality speech enhancement on embedded devices.
Novel Approach of Implementing Psychoacoustic model for MPEG-1 Audioinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Sara Hooker & Sean McPherson, Delta Analytics, at MLconf Seattle 2017MLconf
This document provides information about Delta Analytics, a non-profit organization that provides pro bono data consulting services to social sector organizations. It discusses Delta Analytics' work with Rainforest Connection, including developing machine learning models to detect chainsaw sounds from audio data collected by recycled cell phones deployed in rainforests. Key points discussed include developing convolutional neural networks to classify audio spectrograms, addressing challenges like limited labelled training data and unknown guardian positions, and experiments to estimate the direction of detected sounds.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MULTILINGUAL SPEECH TO TEXT CONVERSION USING HUGGING FACE FOR DEAF PEOPLEIRJET Journal
The document describes a system for multilingual speech-to-text conversion using Hugging Face that aims to assist deaf individuals. The system uses a Transformer-based encoder-decoder model trained on audio datasets. Feature extraction and tokenization are performed on the audio inputs. The model is fine-tuned using Hugging Face and evaluated based on word error rate. A web application allows users to record speech via microphone and see the transcription output. The implemented model achieved a 32.42% word error rate on a Hindi language dataset. The goal is to enable seamless communication for those with hearing impairments across multiple languages.
Conv-TasNet is a convolutional neural network architecture for speech separation that directly operates on the raw audio waveform in the time domain. It surpasses previous approaches that perform separation in the time-frequency domain using magnitude masking. Conv-TasNet uses a convolutional encoder to transform short audio segments into representations, estimates separation masks for each source, and uses a convolutional decoder to reconstruct the sources. Experiments show Conv-TasNet achieves substantially better speech separation than ideal time-frequency masking baselines as measured by metrics like SI-SNRi and SDRi. The best performing model uses a small filter length, increasing numbers of encoder/decoder filters, and deep convolutional layers.
In today's world we know the importance of encryption and privacy and with data being the most prized possession it is more important than ever to protect that data. Therefore for our project we are aiming at using this as our principal objective for protecting signal and audio during transmission.
To do this will use digital watermarking and using a digital image/unique code superimposing the signal and then transposing that image as a watermark on the audio signal.
Watermarking is a technique used to label digital media by hiding copyright or other information into the underlying data. The aim is to create a watermark that must be imperceptible or undetectable by the user and should be robust to attacks and other types of distortion. In our method, the watermark is kept as a digital image or if contingency arises a masked signal copy.
Audio Noise Removal – The State of the Artijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document summarizes a research paper on efficient Transformer-based speech enhancement using long frames and STFT magnitudes. It introduces the task of speech enhancement and issues with learned-domain approaches. The proposed method uses STFT magnitudes as input to an encoder-decoder architecture with a Transformer masker. Experiments show the method achieves equivalent quality and intelligibility scores compared to learned features, while reducing computations by 8x for 10-second utterances. The conclusions are that using STFT magnitudes enables efficient, high-quality speech enhancement on embedded devices.
Novel Approach of Implementing Psychoacoustic model for MPEG-1 Audioinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Sara Hooker & Sean McPherson, Delta Analytics, at MLconf Seattle 2017MLconf
This document provides information about Delta Analytics, a non-profit organization that provides pro bono data consulting services to social sector organizations. It discusses Delta Analytics' work with Rainforest Connection, including developing machine learning models to detect chainsaw sounds from audio data collected by recycled cell phones deployed in rainforests. Key points discussed include developing convolutional neural networks to classify audio spectrograms, addressing challenges like limited labelled training data and unknown guardian positions, and experiments to estimate the direction of detected sounds.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MULTILINGUAL SPEECH TO TEXT CONVERSION USING HUGGING FACE FOR DEAF PEOPLEIRJET Journal
The document describes a system for multilingual speech-to-text conversion using Hugging Face that aims to assist deaf individuals. The system uses a Transformer-based encoder-decoder model trained on audio datasets. Feature extraction and tokenization are performed on the audio inputs. The model is fine-tuned using Hugging Face and evaluated based on word error rate. A web application allows users to record speech via microphone and see the transcription output. The implemented model achieved a 32.42% word error rate on a Hindi language dataset. The goal is to enable seamless communication for those with hearing impairments across multiple languages.
Conv-TasNet is a convolutional neural network architecture for speech separation that directly operates on the raw audio waveform in the time domain. It surpasses previous approaches that perform separation in the time-frequency domain using magnitude masking. Conv-TasNet uses a convolutional encoder to transform short audio segments into representations, estimates separation masks for each source, and uses a convolutional decoder to reconstruct the sources. Experiments show Conv-TasNet achieves substantially better speech separation than ideal time-frequency masking baselines as measured by metrics like SI-SNRi and SDRi. The best performing model uses a small filter length, increasing numbers of encoder/decoder filters, and deep convolutional layers.
In today's world we know the importance of encryption and privacy and with data being the most prized possession it is more important than ever to protect that data. Therefore for our project we are aiming at using this as our principal objective for protecting signal and audio during transmission.
To do this will use digital watermarking and using a digital image/unique code superimposing the signal and then transposing that image as a watermark on the audio signal.
Watermarking is a technique used to label digital media by hiding copyright or other information into the underlying data. The aim is to create a watermark that must be imperceptible or undetectable by the user and should be robust to attacks and other types of distortion. In our method, the watermark is kept as a digital image or if contingency arises a masked signal copy.
Audio Noise Removal – The State of the Artijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Audio Noise Removal – The State of the Artijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Review Paper on Noise Reduction Using Different TechniquesIRJET Journal
This document reviews different techniques for noise reduction in hearing aids. It discusses how digital hearing aids use algorithms like fast Fourier transforms and adaptive filters to separate speech from noise and improve speech comprehension in noisy environments. The document also evaluates different noise reduction algorithms and filter bank designs that have been proposed in previous research to enhance the noise reduction capabilities of hearing aids. The goal of developing improved noise reduction for hearing aids is to help hearing-impaired individuals better understand speech in various real-world settings with background noise.
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
IRJET- Wavelet Transform based SteganographyIRJET Journal
This document proposes an image steganography technique to hide audio signals in images using wavelet transforms. It discusses how discrete wavelet transform (DWT) can be used to decompose images and audio into different frequency subbands. The technique encrypts an audio file (MP3 or WAV) and hides it in the wavelet coefficients of an image. When extracted, the secret audio signal is decrypted. The quality of the stego image and extracted audio is measured using metrics like PSNR, SSIM, SNR, and SPCC. The results show good quality for the steganography technique and that it can withstand various attacks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Survey on Efficient Signal Processing Techniques for Speech EnhancementIRJET Journal
This document provides a survey of various speech enhancement techniques. It discusses five papers that propose different speech enhancement algorithms: 1) Discrete Tchebichef Transform and Discrete Krawtchouk Transform for removing noise using minimum mean square error. 2) Empirical mode decomposition and adaptive centre weighted average filtering that is effective for removing noise components. 3) Adaptive Wiener filtering that adapts the filter transfer function based on speech signal statistics. 4) Compressive sensing based speech enhancement that handles non-sparse noise. 5) Wavelet packet transform and non-negative matrix factorization to emphasize the speech components in each sub-band. The document also discusses speech enhancement using deep neural networks, empirical mode decomposition with Hurst exponent
Snorm–A Prototype for Increasing Audio File Stepwise NormalizationIJERA Editor
This paper introduces a novel concept SNORM (Step NORMalization) for increasing normalization. It is a
prototype algorithm for increasing normalization based on loudness factor of the audio. The function effect
normalization plays a vital role in loudness control. The proposed experiment carried out for increasing
normalization based on step wise increase yield the variations in peaks of the audio file. The experimental
results are shown in the form of graphical analysis of the plot spectrum values of frequency analysis. From the
results, it is clearly apparent that, the normalization values are increased at different levels. The function
SNORM can set a new benchmark in the field of audio industry for the processes of increasing normalization.
SNORM can be substantial in the audio broadcast systems for applications in live audio streaming, news
broadcast, sports coverage, live programming where the loudness control mechanism is essential. For the
selective or predictive loudness control systems SNORM can be effectively applied.
This document discusses using RNN-LSTM algorithms to classify music genres based on audio data. It begins with an abstract describing music genre classification and the use of RNN and LSTM algorithms. It then discusses the existing methods, proposed improvements, and experiments conducted. The proposed system uses MFCC features extracted from audio clips to train an LSTM model to classify songs into 10 genres. It achieves a train accuracy of 94% and test accuracy of 75% after training the model on audio clips split into 3-second segments. The document concludes that LSTM networks are well-suited for this task due to their ability to learn long-term dependencies from audio data.
Performance estimation based recurrent-convolutional encoder decoder for spee...karthik annam
This document discusses a proposed Recurrent-Convolutional Encoder-Decoder (R-CED) network for speech enhancement. The R-CED network aims to overcome challenges with existing methods by estimating the a priori and posteriori signal-to-noise ratios to separate noise from speech. The R-CED consists of convolutional layers with increasing and decreasing numbers of filters to encode and decode features. Performance will be evaluated using metrics like PESQ, STOI, CER, MSE, SNR, and SDR. The proposed method aims to improve speech enhancement accuracy and recover enhanced speech quality compared to other techniques.
IRJET- Musical Instrument Recognition using CNN and SVMIRJET Journal
This document discusses a study that uses convolutional neural networks (CNNs) and support vector machines (SVMs) to recognize musical instruments in audio recordings. The researchers aim to convert audio excerpts to images and use CNNs to classify instruments, then combine the CNN classifications with SVM classifications to improve accuracy. They discuss related work on instrument recognition using other methods. The proposed model uses MFCC features with SVM and passes audio converted to images through four convolutional layers and fully connected layers in the CNN. Combining the CNN and SVM results through weighted averaging is expected to provide higher accuracy than either method alone for classifying instruments in the IRMAS dataset.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
Quality and Distortion Evaluation of Audio Signal by SpectrumCSCJournals
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
This document summarizes a research paper on developing efficient neural network architectures for universal audio source separation. The proposed model, called SuDoRM-RF, uses successive downsampling and resampling of multi-resolution features to extract information from multiple temporal scales with fewer computations. Experimental results show that SuDoRM-RF achieves comparable or better source separation performance than state-of-the-art models while requiring significantly fewer computational resources in terms of FLOPs, memory usage, and training time. Improved and causal variants of SuDoRM-RF are also proposed to further reduce parameters and enable real-time processing.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares several techniques for enhancing the intelligibility of speech signals corrupted by noise. It describes single channel techniques like spectral subtraction, spectral subtraction with oversubtraction, and nonlinear spectral subtraction. It also covers multi-channel techniques such as adaptive noise cancellation and multisensory beamforming. Additionally, it discusses spectral subtraction using adaptive averaging, noise reduction using enhanced Wiener filtering, and other adaptive neuro-fuzzy techniques for speech enhancement. The goal of these techniques is to improve the quality and intelligibility of noisy speech signals.
IRJET- A Novel Hybrid Image Denoising Technique based on Trilateral Filtering...IRJET Journal
The document proposes a novel hybrid image denoising technique based on trilateral filtering and Gaussian conditional random field modeling. It combines trilateral filtering, which is an edge-preserving Gaussian filter, with Gaussian conditional random fields to deal with different noise levels in images. The technique involves first applying trilateral filtering to smooth the image, then using Gaussian conditional random fields on the smoothed image. Experimental results on test images show the proposed technique achieves better denoising performance than traditional trilateral filtering alone, as measured by higher peak signal-to-noise ratios and lower mean squared errors.
IRJET- Voice Command Execution with Speech Recognition and SynthesizerIRJET Journal
The document describes a voice command execution system using speech recognition and text-to-speech synthesis. The proposed system allows users to complete tasks using only voice commands, reducing time delays compared to traditional systems requiring mouse/keyboard input. It recognizes three types of voice commands - social commands for question answering, web commands to access URLs, and shell commands involving file/application directories. A speech synthesizer converts text to speech to provide output to the user. The system aims to enable hands-free computing for disabled users by executing commands with only voice.
Design and implementation of different audio restoration techniques for audio...eSAT Journals
This document summarizes research on designing and implementing different audio restoration techniques for removing distortions like clipping, clicks, and broadband noise from audio signals. It presents methods for declipping audio using sparse representations and frame-based reconstruction. Clicks are addressed using an adaptive filtering method, and broadband noise is reduced via spectral subtraction. The performance of these techniques is evaluated using metrics like SNR and algorithms like OMP. Hardware implementation of click removal is done on a TMS320C6713 DSK board using tools like MATLAB and Code Composer Studio.
1) The document discusses audio compression using Daubechie wavelets. It involves using optimal wavelet selection and quantizing wavelet coefficients along with A-law and U-law companding methods.
2) The key steps of wavelet-based audio compression are thresholding and quantizing wavelet coefficients, then encoding the data to remove redundancy and reduce the number of coefficients.
3) A psychoacoustic model is incorporated to determine inaudible quantization noise levels based on auditory masking principles. The masking thresholds are converted to constraints in the wavelet domain to guide coefficient quantization and selection of an optimal wavelet basis.
This document presents a frequency dependent spectral subtraction method for speech enhancement. The standard spectral subtraction technique advocates subtracting the noise spectrum estimate uniformly over the entire speech spectrum. However, real-world noise is mostly colored and affects speech differently at various frequencies. The proposed method takes this into account by performing noise subtraction in a frequency dependent manner. It divides the spectrum into multiple bands and subtracts noise non-uniformly across the different bands based on the noise characteristics. This leads to improved speech quality and reduced musical noise compared to conventional spectral subtraction. The method is evaluated objectively using measures like perceptual evaluation of speech quality and computational complexity is also considered.
1) The document presents a frame-online DNN-WPE dereverberation method that uses a neural network to directly estimate the power spectral density (PSD) of target speech from observations for use in weighted prediction error (WPE) dereverberation.
2) Experiments on the REVERB challenge and WSJ+VoiceHome datasets show the DNN-based method improves word error rates over unprocessed baselines, particularly for online processing with two microphones.
3) While performance degrades with increased latency constraints and complex background noise, the DNN-WPE still provides an improvement over no enhancement and enables real-time dereverberation.
Audio Noise Removal – The State of the Artijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Review Paper on Noise Reduction Using Different TechniquesIRJET Journal
This document reviews different techniques for noise reduction in hearing aids. It discusses how digital hearing aids use algorithms like fast Fourier transforms and adaptive filters to separate speech from noise and improve speech comprehension in noisy environments. The document also evaluates different noise reduction algorithms and filter bank designs that have been proposed in previous research to enhance the noise reduction capabilities of hearing aids. The goal of developing improved noise reduction for hearing aids is to help hearing-impaired individuals better understand speech in various real-world settings with background noise.
Audio Steganography Coding Using the Discreet Wavelet TransformsCSCJournals
The performance of audio steganography compression system using discreet wavelet transform (DWT) is investigated. Audio steganography coding is the technology of transforming stego-speech into efficiently encoded version that can be decoded in the receiver side to produce a close representation of the initial signal (non compressed). Experimental results prove the efficiency of the used compression technique since the compressed stego-speech are perceptually intelligible and indistinguishable from the equivalent initial signal, while being able to recover the initial stego-speech with slight degradation in the quality .
IRJET- Wavelet Transform based SteganographyIRJET Journal
This document proposes an image steganography technique to hide audio signals in images using wavelet transforms. It discusses how discrete wavelet transform (DWT) can be used to decompose images and audio into different frequency subbands. The technique encrypts an audio file (MP3 or WAV) and hides it in the wavelet coefficients of an image. When extracted, the secret audio signal is decrypted. The quality of the stego image and extracted audio is measured using metrics like PSNR, SSIM, SNR, and SPCC. The results show good quality for the steganography technique and that it can withstand various attacks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Survey on Efficient Signal Processing Techniques for Speech EnhancementIRJET Journal
This document provides a survey of various speech enhancement techniques. It discusses five papers that propose different speech enhancement algorithms: 1) Discrete Tchebichef Transform and Discrete Krawtchouk Transform for removing noise using minimum mean square error. 2) Empirical mode decomposition and adaptive centre weighted average filtering that is effective for removing noise components. 3) Adaptive Wiener filtering that adapts the filter transfer function based on speech signal statistics. 4) Compressive sensing based speech enhancement that handles non-sparse noise. 5) Wavelet packet transform and non-negative matrix factorization to emphasize the speech components in each sub-band. The document also discusses speech enhancement using deep neural networks, empirical mode decomposition with Hurst exponent
Snorm–A Prototype for Increasing Audio File Stepwise NormalizationIJERA Editor
This paper introduces a novel concept SNORM (Step NORMalization) for increasing normalization. It is a
prototype algorithm for increasing normalization based on loudness factor of the audio. The function effect
normalization plays a vital role in loudness control. The proposed experiment carried out for increasing
normalization based on step wise increase yield the variations in peaks of the audio file. The experimental
results are shown in the form of graphical analysis of the plot spectrum values of frequency analysis. From the
results, it is clearly apparent that, the normalization values are increased at different levels. The function
SNORM can set a new benchmark in the field of audio industry for the processes of increasing normalization.
SNORM can be substantial in the audio broadcast systems for applications in live audio streaming, news
broadcast, sports coverage, live programming where the loudness control mechanism is essential. For the
selective or predictive loudness control systems SNORM can be effectively applied.
This document discusses using RNN-LSTM algorithms to classify music genres based on audio data. It begins with an abstract describing music genre classification and the use of RNN and LSTM algorithms. It then discusses the existing methods, proposed improvements, and experiments conducted. The proposed system uses MFCC features extracted from audio clips to train an LSTM model to classify songs into 10 genres. It achieves a train accuracy of 94% and test accuracy of 75% after training the model on audio clips split into 3-second segments. The document concludes that LSTM networks are well-suited for this task due to their ability to learn long-term dependencies from audio data.
Performance estimation based recurrent-convolutional encoder decoder for spee...karthik annam
This document discusses a proposed Recurrent-Convolutional Encoder-Decoder (R-CED) network for speech enhancement. The R-CED network aims to overcome challenges with existing methods by estimating the a priori and posteriori signal-to-noise ratios to separate noise from speech. The R-CED consists of convolutional layers with increasing and decreasing numbers of filters to encode and decode features. Performance will be evaluated using metrics like PESQ, STOI, CER, MSE, SNR, and SDR. The proposed method aims to improve speech enhancement accuracy and recover enhanced speech quality compared to other techniques.
IRJET- Musical Instrument Recognition using CNN and SVMIRJET Journal
This document discusses a study that uses convolutional neural networks (CNNs) and support vector machines (SVMs) to recognize musical instruments in audio recordings. The researchers aim to convert audio excerpts to images and use CNNs to classify instruments, then combine the CNN classifications with SVM classifications to improve accuracy. They discuss related work on instrument recognition using other methods. The proposed model uses MFCC features with SVM and passes audio converted to images through four convolutional layers and fully connected layers in the CNN. Combining the CNN and SVM results through weighted averaging is expected to provide higher accuracy than either method alone for classifying instruments in the IRMAS dataset.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
Quality and Distortion Evaluation of Audio Signal by SpectrumCSCJournals
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
This document summarizes a research paper on developing efficient neural network architectures for universal audio source separation. The proposed model, called SuDoRM-RF, uses successive downsampling and resampling of multi-resolution features to extract information from multiple temporal scales with fewer computations. Experimental results show that SuDoRM-RF achieves comparable or better source separation performance than state-of-the-art models while requiring significantly fewer computational resources in terms of FLOPs, memory usage, and training time. Improved and causal variants of SuDoRM-RF are also proposed to further reduce parameters and enable real-time processing.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares several techniques for enhancing the intelligibility of speech signals corrupted by noise. It describes single channel techniques like spectral subtraction, spectral subtraction with oversubtraction, and nonlinear spectral subtraction. It also covers multi-channel techniques such as adaptive noise cancellation and multisensory beamforming. Additionally, it discusses spectral subtraction using adaptive averaging, noise reduction using enhanced Wiener filtering, and other adaptive neuro-fuzzy techniques for speech enhancement. The goal of these techniques is to improve the quality and intelligibility of noisy speech signals.
IRJET- A Novel Hybrid Image Denoising Technique based on Trilateral Filtering...IRJET Journal
The document proposes a novel hybrid image denoising technique based on trilateral filtering and Gaussian conditional random field modeling. It combines trilateral filtering, which is an edge-preserving Gaussian filter, with Gaussian conditional random fields to deal with different noise levels in images. The technique involves first applying trilateral filtering to smooth the image, then using Gaussian conditional random fields on the smoothed image. Experimental results on test images show the proposed technique achieves better denoising performance than traditional trilateral filtering alone, as measured by higher peak signal-to-noise ratios and lower mean squared errors.
IRJET- Voice Command Execution with Speech Recognition and SynthesizerIRJET Journal
The document describes a voice command execution system using speech recognition and text-to-speech synthesis. The proposed system allows users to complete tasks using only voice commands, reducing time delays compared to traditional systems requiring mouse/keyboard input. It recognizes three types of voice commands - social commands for question answering, web commands to access URLs, and shell commands involving file/application directories. A speech synthesizer converts text to speech to provide output to the user. The system aims to enable hands-free computing for disabled users by executing commands with only voice.
Design and implementation of different audio restoration techniques for audio...eSAT Journals
This document summarizes research on designing and implementing different audio restoration techniques for removing distortions like clipping, clicks, and broadband noise from audio signals. It presents methods for declipping audio using sparse representations and frame-based reconstruction. Clicks are addressed using an adaptive filtering method, and broadband noise is reduced via spectral subtraction. The performance of these techniques is evaluated using metrics like SNR and algorithms like OMP. Hardware implementation of click removal is done on a TMS320C6713 DSK board using tools like MATLAB and Code Composer Studio.
1) The document discusses audio compression using Daubechie wavelets. It involves using optimal wavelet selection and quantizing wavelet coefficients along with A-law and U-law companding methods.
2) The key steps of wavelet-based audio compression are thresholding and quantizing wavelet coefficients, then encoding the data to remove redundancy and reduce the number of coefficients.
3) A psychoacoustic model is incorporated to determine inaudible quantization noise levels based on auditory masking principles. The masking thresholds are converted to constraints in the wavelet domain to guide coefficient quantization and selection of an optimal wavelet basis.
This document presents a frequency dependent spectral subtraction method for speech enhancement. The standard spectral subtraction technique advocates subtracting the noise spectrum estimate uniformly over the entire speech spectrum. However, real-world noise is mostly colored and affects speech differently at various frequencies. The proposed method takes this into account by performing noise subtraction in a frequency dependent manner. It divides the spectrum into multiple bands and subtracts noise non-uniformly across the different bands based on the noise characteristics. This leads to improved speech quality and reduced musical noise compared to conventional spectral subtraction. The method is evaluated objectively using measures like perceptual evaluation of speech quality and computational complexity is also considered.
1) The document presents a frame-online DNN-WPE dereverberation method that uses a neural network to directly estimate the power spectral density (PSD) of target speech from observations for use in weighted prediction error (WPE) dereverberation.
2) Experiments on the REVERB challenge and WSJ+VoiceHome datasets show the DNN-based method improves word error rates over unprocessed baselines, particularly for online processing with two microphones.
3) While performance degrades with increased latency constraints and complex background noise, the DNN-WPE still provides an improvement over no enhancement and enables real-time dereverberation.
WaveNet is a generative model for raw audio developed by Google DeepMind that uses dilated causal convolutions to capture long-term dependencies in audio. It can generate speech that is subjectively natural and can be conditioned on speaker identity. The model was also shown to generate music from specific instruments and genres with varying levels of success depending on the conditioning information provided. Key aspects of the model include causal dilated convolutions to exponentially increase the receptive field, gated activation units, and residual and skip connections.
A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech E...ssuser849b73
A single model is presented that can perform acoustic echo cancellation, speech enhancement, and speech separation jointly using a conformer architecture. The model takes as input a reference signal, noise context, and target speaker embedding. Evaluation shows the joint model achieves performance close to task-specific models while significantly improving the noise robustness of a large-scale ASR system.
Wavesplit is an end-to-end speech separation model that leverages speaker identities during training. It uses a speaker clustering network to produce speaker representations and a separation network conditioned on the representations. During training, speaker labels are used to learn discriminative representations and optimize reconstruction quality. At test time, the model relies on clustering the representations to assign speakers without labels. The model achieves state-of-the-art results on speech separation benchmarks and can also be applied to other source separation tasks like separating heart rates from abdominal recordings.
EEND-SS is a joint model that performs speaker diarization, speech separation, and speaker counting in an end-to-end manner. It combines the EEND-EDA diarization model and Conv-TasNet separation model. EEND-SS uses a multi-task learning approach and multiple 1x1 convolution layers to estimate separation masks for a flexible number of speakers. Experimental results on the LibriMix dataset show it outperforms baseline methods in metrics like SDRi and DER, handling both a fixed and variable number of speakers.
SepFormer and DPTNet are Transformer-based models for monaural speech separation that achieve state-of-the-art performance. SepFormer uses dual-path Transformers to model short and long-term dependencies without RNNs, allowing parallel processing. DPTNet introduces an improved Transformer with a recurrent layer to directly model contextual information in speech sequences. Experiments on standard datasets show SepFormer achieves SOTA results and is faster to train and infer than RNN baselines like DPRNN. Both models obtain competitive separation but SepFormer has advantages in parallelization and efficiency due to its RNN-free design.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
VARIABLE FREQUENCY DRIVE. VFDs are widely used in industrial applications for...PIMR BHOPAL
Variable frequency drive .A Variable Frequency Drive (VFD) is an electronic device used to control the speed and torque of an electric motor by varying the frequency and voltage of its power supply. VFDs are widely used in industrial applications for motor control, providing significant energy savings and precise motor operation.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
1. Audio Inpainting
with
Generative Adversarial Network
Authors: Pirmin Philipp Ebner, Amr Eltelt
Published in Ar Xiv [ eess. AS ], March 13, 2020
Presenter : Kuan Hsun Ho
Date : 2021/10/08
2. Outline
1. Introduction
-Background, Objective
2. Architecture
-GAN, WGAN, Long/short Boarders, Dual Discriminators WGAN, Loss Function
3. Methodology
-Dataset, Signal Preprocessing, Training, Evaluation Metrics
4. Results & Discussion
-Different Network, Different Dataset, Different Training Steps
5. Conclusion & Outlook
6. References
4. ● Corrupted audio files, lost information in audio transmission (e.g. VoIP), and
audio signals locally contaminated by noise are highly important problems in
various audio processing tasks like in music enhancement and restoration.
● Loss of the connection in audio transmission -> data loss beyond hundred
milliseconds. This has highly unpleasant consequences for a listener, and it is
hardly feasible to reconstruct the lost content from local information only.
● The audio features are high dimensional, complex and non-correlated ->
applying state-of-the art models in image or video inpaintings tends not to
work well[2].
● Restoring of lost information in audio : waveform substitution [5], audio
inter/extrapolation [6,7], or audio inpainting [8].
Introduction
Background
5. ● Reconstruction is usually aimed at providing a coherent and meaningful information
while preventing audible artifacts so that the listener remains unaware of any occurred
problem.
● Gaps < 50ms, small gaps, no synthesis needed, consider only over statistics
=> Uses sparsity-based techniques.
Otherwise, long gaps, regressive modeling[7] / sinusoidal modeling[10,11] / GAN[13] is
used.
● Audio signal are composed of fundamental freq and overtones.
● Having multiple different instrument in the training set, a higher frequency
spectrum has to be covered and the generated audio signal is stronger influenced by
noise.
Introduction
Background
6. ● Audio inpainting of long gap (500~550ms) audio content.
● Focus on waveform strategies rather than spectrogram or multimodal strategies.
● Using WGAN in generating audio with
1. global coherence.
2. good audio quality.
3. different type of instruments from different dataset.
=> ability to adapt to different audio signals
● Finding a optimal model setting that is generalized rather than overfitted to specific dataset.
● There is no specific metric that can be used to evaluate the quality of generated audio signal
and therefore we depend on human judgement using the objective difference grading (ODG)
technique for evaluation.
● It is clear that in this unsupervised setting, our goal is not to generate a perfectly matching
audio similar to ground truth.
Introduction
Objective
8. ● GANs rely on two competing neural networks trained simultaneously in a two-player min-max
game: The generator produces new data from samples of a random variable; The
discriminator attempts to distinguish between these generated and real data.
● Generator : To fool the discriminator
Discriminator : Learn to better classify real and generated (fake) data.
● Advantages of GAN-based approaches is the rapid and straightforward sampling of large
amounts of audio.
● Several GAN approaches have been investigate to fill gaps in audio like WaveGAN [4] or
TiFGAN [14].
Architecture
GAN
9. ● Improve the stability of learning.
○ Both generator and discriminator have to learn in correct directions.
● Get rid of problems like mode collapse.
○ Generator and discriminator have to be equally robust.
● Provide meaningful learning curves useful for debugging and hyperparameter
searches.
● Guarantee for data diversity.
Architecture
WGAN
11. ● The approach applied here is the extraction of the short-range borders and the long-range borders of
the missing audio content.
● Close neighbor audio have proven success to inpaint short period missing audio, however it fails for long
periods.
● A larger range of neighboring audio can tell us something about the long missing audio content.
Architecture
Long/Short Boarders
Boarders 1 length = 1+0.5+1 = 2.5s.
Boarders 2 length = 3+0.5+3 = 6.5s.
13. ● To make GAN perform well, we minimize the total loss of both discriminators and generator,
and expect convergence.
Architecture
Loss Function
This figure shows the performance of training the total D-loss and total G-loss for different dataset.
15. ● We considered instrument sounds including piano, acoustic guitar and piano together with
a string orchestra using three different dataset.
1. The PIANO dataset contains only piano samples.
2. The SOLO dataset contains different instruments recording including accordion, acoustic
guitar, cello, flute, saxophone, trumpet, violin, and xylophone. In our work only the
acoustic guitar dataset.
3. The MAESTRO dataset contains recordings from the International Piano-e-Competition.
It is a piano performance competition where virtuoso pianists perform on Yamaha
claviers together with a string orchestra. The data must be splitted in training / validation
/ testing so that the same composition, even if performed by multiple contestants, does
not appear in multiple subsets. (Important!)
Methodology
Dataset
18. ● Both Generator and Discriminator shares same hyperparameter :
Learning rate : 1e-4
Optimizer : Adam
Batch size : 64 MAESTRO
Iteration ( steps )
PIANO, SOLO
● Parameters : 53248
● 3 datasets x 2 model = 6 training
● Figure besides shows comparison of generated audio
and real audio.
Methodology
Training
WGAN : 73 k
D2WGAN : 56.3 k
WGAN : 40 k
D2WGAN : 53.6 k
19. ● Using the signal-to-noise ratios (SNRs) applied to the time-domain waveforms and
magnitude spectrograms is not an appropriate indicator to evaluate the model.
● Therefore, we conducted a user study evaluating the inpainting quality by means of
objective difference grades (ODG [22]).
● 50 samples of 6.5 s length for each dataset and model, in total 150 samples per model,
evaluate them by two independent person without knowing the source of the samples and
evaluate using ODG.
Methodology
Evaluation Metrics
21. Results & Discussion
Different Network
For PIANO,
● D-loss and G-loss in both models
converge.
● No overfitting occurs.
=> WGAN slightly outperforms.
For SOLO,
● D-loss in WGAN hasn’t converged.
● G-loss in both models converge.
=> D2WGAN highly outperforms.
22. ● ODG table is as follows.
Results & Discussion
Different Network
+0.02 +0.08 +0.2 +0.1
-0.33 -0.07
23. ● Inpainting quality : SOLO > PIANO > MAESTRO
● The SOLO dataset offers much more inpainting techniques and sound variations than the
PIANO dataset.
Guitar, 144 tones, covers four octaves. Piano, 88 tones, covers seven octaves.
● Lower frequency signal represents information at a lower rate (needs lesser samples to
reconstruct it) whereas a high frequency signal represents information at a faster rate (and hence
needs more samples while reconstruction).
Results & Discussion
Different Dataset
● Human ears are more sensitive in high frequencies, we can know it
from k-weighted filter.
● The MAESTRO dataset, a combination of piano together with a
string orchestra, is much harder to train. Also, it is a huge dataset of
201 hours of music and there is not only one single instrument
playing at the same time.
25. ● Improves ODG performance by training our network longer.
● The D2WGAN model was trained on the PIANO dataset for 140000 steps and tested both on the
PIANO and MAESTRO dataset.
● The D2WGAN model showed on both dataset a huge improvement. The results indicate that we
didn’t overfit the D2WGAN model and got excellent performance (both mean & std).
● Explanation may be that the model interpreted the orchestra mainly as background noise. Thus,
small noise in the inpainting part will be stronger smoothed and therefore the impairment is
more perceptible but not annoying.
Results & Discussion
Different Training Steps
27. ● Audio inpainting of long gap (500~550ms) audio content.
● Focus on waveform strategies rather than spectrogram or multimodal strategies.
● Using WGAN in generating audio with
1. global coherence.
2. good audio quality.
3. different type of instruments from different dataset.
=> ability to adapt to different audio signals
● Finding a optimal model setting that is generalized rather than overfitted to specific dataset.
● There is no specific metric that can be used to evaluate the quality of generated audio signal
and therefore we depend on human judgement using the objective difference grading (ODG)
technique for evaluation.
● It is clear that in this unsupervised setting, our goal is not to generate a perfectly matching
audio similar to ground truth.
Introduction
Objective
28. ● We analysed long gap (500 - 550 ms) audio content inpainting using WGAN and D2WGAN.
● The D2WGAN model is a new proposed architecture improvement to inpaint the missing
audio content using a short-range and a long-range borders.
● The new proposed D2WGAN model slightly outperforms the classic WGAN model for all
three different dataset (piano, guitar, piano and orchestra).
● The long range borders combined with short range borders can potential provide
information in correlation with the gap content.
● A better improvement was observed for the SOLO and MAESTRO.
● The poor results for the PIANO can be illustrated by the fact that it lacks audible variation in
sound.
● As we aim for generalized solution, we need to have a dataset with a generalized audio
content.
Conclusion & Outlook
29. ● By increasing the training step, the D2WGAN has significantly improved performance
without overfitting.
● Better results can be achieved for audio dataset where a particular instrument is
accompanist by other instruments if we train the network only on this particular instrument
and neglecting the other instruments.
● As for future work, few topics can be done
1. playing around with the boarder length
2. adding more layers to the convolution operation or changing filters sizes
3. exploring multimodal strategies where we combine waveform and spectrogram
images
4. finding datasets that helps strengthen reconstruction of high frequency
5. training on a wide range of music instruments
Conclusion & Outlook
31. [1] N. Perraudin, N. Holighaus, P. Majdak, and P. Balazs, “Inpainting of long audio segments with similarity graphs,” IEEE/ACM Transactions on Audio,
Speech, and Language Processing, 2018.
[2] Y.-L. Chang, K.-Y. Lee, P.-Y. Wu, H.-y. Lee, and W. Hsu, “Deep long audio inpainting,” arXiv:1911.06476v1, 2019.
[3] A.VanDenOord,S.Dieleman,H.Zen,K.Simonyan,O.Vinyals,A.Graves,N.Kalchbrenner, A. W. Senior, and K. Kavukcuoglu, “Wavenet: A generative
model for raw audio.,” in SSW, p. 125, 2016.
[4] C. Donahue, J. McAuley, and M. Puckette, “Synthesizing audio with generative adversarial networks,” arXiv:1802.04208, 2018.
[5] D. Goodman, G. Lockhart, O. Wasem, and W.-C. Wong, “Waveform substitution techniques for recovering missing speech segments in packed voice
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[6] I. Kauppinen, J. Kauppinen, and P. Saarinen, “A method for long extrapolation of audio signals,” Journal of the Audio Engineering Society, vol. 49, no.
12, pp. 1167–1180, 2001.
[7] W.Etter,“Restoration of a discrete-time signal segment by interpolation based on the left-sided and right-sided autoregressive parameters,” IEEE
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[8] A. Adler, V. Emiya, M. Jafari, M. Elad, R. Gribonval, and M. Plumbley, “Audio inpainting,” IEEE Transactions on Audio, Speech and Language
Processing, vol. 20, no. 3, pp. 922–932, 2012.
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Representations, 2013.
References
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[11] A. Lukin and J. Todd, “Parametric interpolation of gaps in audio signals,” Audio Engineering Society Convention, vol. 125, 2008.
[12] Y.Bahat,Y.Schechner,andM.Elad,“Self-content-basedaudioinpainting,”SignalProcessing, vol. 111, pp. 61–72, 2015.
[13] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in
neural information processing sys- tems, pp. 2672–2680, 2014.
[14] A. Marafioti, N. Holighaus, N. Perraudin, and P. Majdak, “Adversarial generation of time- frequency features with application in audio synthesis,”
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Representations, 2017.
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References
33. CREDITS: This presentation template was created
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infographics & images by Freepik
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