Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applications. So, there exists an alternative solution i.e. to implement the filters in frequency domain. AEC has so many applications in wide variety of problems in industrial operations, manufacturing and consumer products. Here in this paper, a comparative analysis of different acoustic echo cancellation techniques i.e. Frequency domain adaptive filter (FDAF), Least mean square (LMS), Normalized least mean square (NLMS) &Sign error (SE) is presented. The results are compared with different values of step sizes and the performance of these techniques is measured in terms of Error rate loss enhancement (ERLE), Mean square error (MSE)& Peak signal to noise ratio (PSNR).
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.
Comparison of different Sub-Band Adaptive Noise Canceller with LMS and RLSijsrd.com
Sub-band adaptive noise is employed in various fields like noise cancellation, echo cancellation and system identification etc. It reduces computational complexity and improve convergence rate. In this paper we perform different Sub-band noise cancellation method for simulation. The Comparison with different algorithm has been done to find out which one is best.
Analysis the results_of_acoustic_echo_cancellation_for_speech_processing_usin...Venkata Sudhir Vedurla
This document presents an analysis of acoustic echo cancellation for speech processing using the LMS adaptive filtering algorithm. It begins with an abstract that outlines the challenges of conventional echo cancellation techniques and the need for a computationally efficient, rapidly converging algorithm. It then provides background on acoustic echo, the principles of echo cancellation, discrete time signals, speech signals, and an overview of the LMS adaptive filtering algorithm and its application to echo cancellation. The document analyzes the performance of the LMS algorithm for echo cancellation by examining how the step size parameter affects convergence and steady state error. It concludes that the LMS algorithm is well-suited for echo cancellation due to its computational simplicity, though the step size must be carefully selected for optimal performance
This document describes an adaptive Kalman filter implementation for video denoising. It proposes processing video frames independently in the spatial domain and then applying an adaptive temporal Kalman filter to each pixel sequence to reduce complexity. An adaptive Kalman filter is used which can adjust its parameters based on noise statistics variations and detected motions between frames. The algorithm is tested through MATLAB simulation on sample video frames, showing it produces a denoised output with reduced noise while still responding to changes in pixel values over time. The design considerations for FPGA implementation focus on using fixed-point arithmetic and shift operations instead of division to optimize for the FPGA hardware.
Simulation and hardware implementation of Adaptive algorithms on tms320 c6713...Raj Kumar Thenua
Raj Kumar Thenua presented his dissertation on "Simulation and Hardware Implementation of NLMS algorithm on TMS320C6713 Digital Signal Processor". The presentation outlined the introduction to adaptive noise cancellation, various adaptive algorithms like LMS, NLMS and RLS. MATLAB simulation results were analyzed for tone signals comparing the performance of algorithms. The best performing NLMS algorithm was implemented on a TMS320C6713 DSP processor. Results for tone signals and ECG signals showed improvement in SNR. The dissertation concluded the real-time implementation enabled analysis of actual signals and provided better noise reduction than simulation.
An Advanced Implementation of a Digital Artificial Reverberatora3labdsp
This paper proposes an enhanced hybrid reverberator that uses both measured impulse responses and synthesized impulse responses to model reverberation effects. It develops an automatic procedure to set the parameters of the hybrid reverberator by analyzing the mixing time of measured impulse responses and minimizing a loss function in the cepstral domain. Experimental results show the proposed method produces higher quality reverberation effects than a previous method, with only a small increase in computational cost, as confirmed by listening tests.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
This document compares the performance of LMS and RLS adaptive FIR filters for noise cancellation. It finds that as simulation time increases, the LMS filter more effectively removes noise from signals compared to the RLS filter. The LMS filter converges faster but the RLS filter provides better noise reduction, though not complete removal even at long simulation times. The LMS filter requires less complexity and is better for hardware implementations.
This document summarizes a presentation on multirate digital signal processing. Multirate systems involve processing signals at different sampling rates, using operations like decimation to lower the sampling rate and interpolation to increase it. Decimation involves downsampling by discarding samples, while interpolation involves upsampling by inserting zeros. These operations are used for applications like sampling rate conversion, audio/video encoding, and communications systems. Key aspects of multirate signal processing discussed include anti-alias filtering, sampling rate conversion using cascaded decimation and interpolation, and choosing optimal filter designs.
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.
Comparison of different Sub-Band Adaptive Noise Canceller with LMS and RLSijsrd.com
Sub-band adaptive noise is employed in various fields like noise cancellation, echo cancellation and system identification etc. It reduces computational complexity and improve convergence rate. In this paper we perform different Sub-band noise cancellation method for simulation. The Comparison with different algorithm has been done to find out which one is best.
Analysis the results_of_acoustic_echo_cancellation_for_speech_processing_usin...Venkata Sudhir Vedurla
This document presents an analysis of acoustic echo cancellation for speech processing using the LMS adaptive filtering algorithm. It begins with an abstract that outlines the challenges of conventional echo cancellation techniques and the need for a computationally efficient, rapidly converging algorithm. It then provides background on acoustic echo, the principles of echo cancellation, discrete time signals, speech signals, and an overview of the LMS adaptive filtering algorithm and its application to echo cancellation. The document analyzes the performance of the LMS algorithm for echo cancellation by examining how the step size parameter affects convergence and steady state error. It concludes that the LMS algorithm is well-suited for echo cancellation due to its computational simplicity, though the step size must be carefully selected for optimal performance
This document describes an adaptive Kalman filter implementation for video denoising. It proposes processing video frames independently in the spatial domain and then applying an adaptive temporal Kalman filter to each pixel sequence to reduce complexity. An adaptive Kalman filter is used which can adjust its parameters based on noise statistics variations and detected motions between frames. The algorithm is tested through MATLAB simulation on sample video frames, showing it produces a denoised output with reduced noise while still responding to changes in pixel values over time. The design considerations for FPGA implementation focus on using fixed-point arithmetic and shift operations instead of division to optimize for the FPGA hardware.
Simulation and hardware implementation of Adaptive algorithms on tms320 c6713...Raj Kumar Thenua
Raj Kumar Thenua presented his dissertation on "Simulation and Hardware Implementation of NLMS algorithm on TMS320C6713 Digital Signal Processor". The presentation outlined the introduction to adaptive noise cancellation, various adaptive algorithms like LMS, NLMS and RLS. MATLAB simulation results were analyzed for tone signals comparing the performance of algorithms. The best performing NLMS algorithm was implemented on a TMS320C6713 DSP processor. Results for tone signals and ECG signals showed improvement in SNR. The dissertation concluded the real-time implementation enabled analysis of actual signals and provided better noise reduction than simulation.
An Advanced Implementation of a Digital Artificial Reverberatora3labdsp
This paper proposes an enhanced hybrid reverberator that uses both measured impulse responses and synthesized impulse responses to model reverberation effects. It develops an automatic procedure to set the parameters of the hybrid reverberator by analyzing the mixing time of measured impulse responses and minimizing a loss function in the cepstral domain. Experimental results show the proposed method produces higher quality reverberation effects than a previous method, with only a small increase in computational cost, as confirmed by listening tests.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
This document compares the performance of LMS and RLS adaptive FIR filters for noise cancellation. It finds that as simulation time increases, the LMS filter more effectively removes noise from signals compared to the RLS filter. The LMS filter converges faster but the RLS filter provides better noise reduction, though not complete removal even at long simulation times. The LMS filter requires less complexity and is better for hardware implementations.
This document summarizes a presentation on multirate digital signal processing. Multirate systems involve processing signals at different sampling rates, using operations like decimation to lower the sampling rate and interpolation to increase it. Decimation involves downsampling by discarding samples, while interpolation involves upsampling by inserting zeros. These operations are used for applications like sampling rate conversion, audio/video encoding, and communications systems. Key aspects of multirate signal processing discussed include anti-alias filtering, sampling rate conversion using cascaded decimation and interpolation, and choosing optimal filter designs.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
The document provides an overview of adaptive filters. It discusses that adaptive filters are digital filters that have self-adjusting characteristics to changes in input signals. They have two main components: a digital filter with adjustable coefficients and an adaptive algorithm. Common adaptive algorithms are LMS and RLS. Adaptive filters are used for applications like noise cancellation, system identification, channel equalization, and signal prediction. The key aspects of adaptive filter theory and algorithms like LMS, RLS, Wiener filters are also covered.
Simulation Study of FIR Filter based on MATLABijsrd.com
First, the rapid design of FIR digital filter was completed by using the Signal Processing Toolbox FDA Tool, the case filter design of a composite signal by filtering, to prove that the content filter designed for filtering. MATLAB and Simulink programs of the filter were used to verify the performance of the filter in MATLAB. Experimental results show that the low-pass filter filters the high frequency component of input signals mixed. Comparison of two types of simulation, the latter method was more convenient quickly, and reduces the workload.
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
Analysis of different FIR Filter Design Method in terms of Resource Utilizati...ijsrd.com
In this paper fully parallel FIR filters are designed with different design method on FPGA for resource utilization and response analysis. fully parallel band-pass FIR filters with same specification designed and simulated on ISE. The suggested implementations are synthesized with Xilinx ISE 14.2 version. Results show comparison of three different filter design methods in terms of resource utilization.
Hybrid Reverberation Algorithm: a Practical Approacha3labdsp
Reverberation is a well known eect that has an important role in our listening experience. Reverberation changes positively the perception of the sound, adding fullness and sense of space. Generally, two approaches are employed for articial reverberation: the desired signal can be obtained by convolving the input signal
with a measured impulse response (IR) or by synthetic techniques based on recursive lter structures. Taking into account the advantages of both approaches, a hybrid articial reverberation algorithm is presented aiming to reproduce the acoustic behaviour of real environment with a low computational load. More in detail, the early reflections are derived from a real impulse response, truncated considering the calculated mixing time, and the reverberation tail is obtained using an IIR lter network. The parameters dening this structure are automatically derived from the analyzed impulse response, using a minimization criteria based on Simultaneous Perturbation Stochastic Approximation (SPSA). The effectiveness of the proposed approach has been proved taking into account a real Italian Theatre impulse response providing comparison with the existing state-of-art techniques in terms of objective and subjective measures.
The document discusses adaptive filters, which can automatically adjust their parameters to filter signals whose exact frequency response is unknown. It defines adaptive filters as having an input signal, filter structure, adjustable parameters, and adaptive algorithm. The goal of adaptive filtering is to minimize the error between the filter's output and a desired response. It describes common adaptive filtering problems and solutions like using gradient descent algorithms and the mean squared error cost function to adjust the filter parameters over time and minimize error.
Acoustic echo cancellation using nlms adaptive algorithm ranbeerRanbeer Tyagi
The document discusses acoustic echo cancellation using the NLMS adaptive algorithm. It introduces the acoustic echo problem in hands-free communication systems and how echo cancellation works by using an adaptive filter to generate an echo replica that is subtracted from the echo signal. It then describes the NLMS adaptive algorithm and how it offers improved convergence over LMS with low computational complexity. Simulation results show NLMS effectively cancels echo. Future work topics are enhancing performance in noisy and double-talk conditions.
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cance...CSCJournals
We have designed and simulated an acoustic echo cancellation system for conferencing. This system is based upon a least-mean-square (LMS) adaptive algorithm and uses multi filter technique. A comparative study of both structure has been carried out and it is found that this new multi-filter converge faster than similar single long adaptive filter. Index Terms: LMS,Multiple sub filter ,Echo cancellation
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). It presents the following:
1. Fullband adaptive filters can have slow convergence due to correlated speech input and long echo path impulse responses. Subband adaptive filters (SAFs) address this by using individual adaptive filters in spectral subbands.
2. Adaptive combination of SAFs provides a way to achieve both fast convergence and small steady-state error. It independently adapts filters with different step sizes, then combines them using a mixing parameter adapted by stochastic gradient descent.
3. The proposed method adaptively combines NSAFs in subbands. It uses a large step size filter for fast convergence and a
FOLDED ARCHITECTURE FOR NON CANONICAL LEAST MEAN SQUARE ADAPTIVE DIGITAL FILT...VLSICS Design
Power consumption reduction is transpiring drift in area of VLSI digital signal processing. This gives rise
to need of minimization of silicon area which is done by folding algorithm. As silicon area decreases power
consumption of a circuit decreases. Folding is an algorithm which reduces silicon chip area by combining
various arithmetic operations into one operation by time scheduling technique. It is applied on iterative
data flow graph with appropriate folding set. Least mean square algorithm alters coefficients of Adaptive
filter in order to achieve desired output. Proposed work is focused on design of efficient VLSI architecture
for LMS adaptive filter aims at reducing mainly area which results in power consumption reduction and
hardware complexity. LMS filter structure used here is called non-canonical as transpose FIR structure is
used. Results show that numbers of adders are reduced by 37.5 % and multipliers by 33.33% without
changing characteristics of filter.
Asymmetric recursive Gaussian filtering for space-variant artificial bokehTuan Q. Pham
This document describes an asymmetric recursive Gaussian filter for space-variant artificial bokeh. The filter approximates two-dimensional space-variant blur using separable one-dimensional Gaussian filtering along the x- and y- dimensions. Within each dimension, the Gaussian filter is approximated by parallel forward and backward infinite impulse response (IIR) filters. The filter reduces intensity leakage at blur discontinuities by modifying the blur sigma of the IIR filters differently for the forward and backward passes as they approach discontinuities, resulting in an asymmetric space-variant filter. This asymmetric recursive filter is able to produce visually pleasing background blur for scenes with contents at different depths without smearing artifacts.
This document discusses filter banks, which are arrays of bandpass filters that separate an input signal into multiple sub-band components. It covers types of filter banks like analysis and synthesis banks, as well as uniform and non-uniform filter banks. Two-channel and polyphase two-channel filter banks are explained in more detail. Applications like signal compression and graphic equalizers are also mentioned. Lifting approaches for implementing filters efficiently are briefly outlined.
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.
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
This document discusses and compares various adaptive filtering algorithms for noise cancellation, including LMS, NLMS, RLS, and APA. It finds that RLS converges the fastest but has the highest complexity, while LMS converges the slowest but is simplest. NLMS and APA provide a balance between convergence speed and complexity. The document implements these algorithms on a noise cancellation problem and finds that RLS achieves the highest SNR improvement and best noise cancellation, followed by APA, NLMS, and LMS.
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...Raj Kumar Thenua
This document is a dissertation submitted by Raj Kumar Thenua for the degree of Master of Technology in electronics and communication engineering from Sobhasaria Engineering College in Sikar, India. The dissertation investigates simulation and hardware implementation of the NLMS adaptive filtering algorithm on a TMS320C6713 digital signal processor. It includes an introduction, literature review, overview of adaptive filtering algorithms like LMS, NLMS and RLS, MATLAB simulation of these algorithms, Simulink model design for hardware implementation, real-time implementation on the TMS320C6713 processor and results and discussion.
Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering
The document summarizes speech recognition front-end technologies including voice activity detection (VAD) and speech enhancement. It discusses conventional signal processing based approaches and more recent deep learning based methods. For VAD, it describes adaptive context attention models that can dynamically adjust the context used based on noise type and SNR. For speech enhancement, it proposes a two-step neural network approach consisting of a prior network that makes multiple predictions from noisy features and a post network that combines these using a boosting method to produce enhanced features, allowing end-to-end training without an explicit masking step. The approach aims to better exploit neural network modeling power while reducing computation cost compared to conventional methods or single-step deep learning frameworks.
The document discusses the discrete cosine transform (DCT) and compares it to the discrete Fourier transform (DFT). Some key points:
- DCT provides better energy compaction than DFT, making it useful for image/signal compression applications. It transforms a signal from the spatial domain to the frequency domain.
- DCT is a real-valued transform derived from DFT, but is more computationally efficient as it avoids redundant complex calculations for real input data.
- The one-dimensional and two-dimensional DCT transforms are defined. DCT has properties like separability and invertibility like DFT.
- DCT coefficients represent the signal's frequency content, with low frequencies concentrated in
This document summarizes a research paper that compares different digital filtering techniques for removing noise from electrocardiogram (ECG) signals. It describes how finite impulse response (FIR) filters were designed using various windowing techniques, including rectangular, Hamming, Hanning, and Blackman windows. Infinite impulse response (IIR) filters and wavelet transforms were also evaluated for denoising ECG signals. The performance of the different filtering approaches were compared based on the power spectral density and average power of the signals before and after filtering. The paper found that an FIR filter designed with the Kaiser window showed the best results for noise removal from ECG signals.
This document presents a method for updating the memory table of a distributed arithmetic (DA) adaptive FIR filter without compromising convergence speed or requiring additional memory resources. DA reduces computational workload by precomputing and storing filter coefficient sums in a lookup table (LUT). However, updating the table is challenging for adaptive filters. The proposed method exploits temporal locality and subexpression sharing to fully update the table with each new sample. It reduces computations and maintains fast convergence. The method enables efficient implementation of computationally-intensive discrete wavelet transforms using DA's parallel architecture and maximal LUT utilization. Simulation results show the discrete wavelet transform can be computed with high LUT utilization using this adaptive DA filter design.
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.
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.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
The document provides an overview of adaptive filters. It discusses that adaptive filters are digital filters that have self-adjusting characteristics to changes in input signals. They have two main components: a digital filter with adjustable coefficients and an adaptive algorithm. Common adaptive algorithms are LMS and RLS. Adaptive filters are used for applications like noise cancellation, system identification, channel equalization, and signal prediction. The key aspects of adaptive filter theory and algorithms like LMS, RLS, Wiener filters are also covered.
Simulation Study of FIR Filter based on MATLABijsrd.com
First, the rapid design of FIR digital filter was completed by using the Signal Processing Toolbox FDA Tool, the case filter design of a composite signal by filtering, to prove that the content filter designed for filtering. MATLAB and Simulink programs of the filter were used to verify the performance of the filter in MATLAB. Experimental results show that the low-pass filter filters the high frequency component of input signals mixed. Comparison of two types of simulation, the latter method was more convenient quickly, and reduces the workload.
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
Analysis of different FIR Filter Design Method in terms of Resource Utilizati...ijsrd.com
In this paper fully parallel FIR filters are designed with different design method on FPGA for resource utilization and response analysis. fully parallel band-pass FIR filters with same specification designed and simulated on ISE. The suggested implementations are synthesized with Xilinx ISE 14.2 version. Results show comparison of three different filter design methods in terms of resource utilization.
Hybrid Reverberation Algorithm: a Practical Approacha3labdsp
Reverberation is a well known eect that has an important role in our listening experience. Reverberation changes positively the perception of the sound, adding fullness and sense of space. Generally, two approaches are employed for articial reverberation: the desired signal can be obtained by convolving the input signal
with a measured impulse response (IR) or by synthetic techniques based on recursive lter structures. Taking into account the advantages of both approaches, a hybrid articial reverberation algorithm is presented aiming to reproduce the acoustic behaviour of real environment with a low computational load. More in detail, the early reflections are derived from a real impulse response, truncated considering the calculated mixing time, and the reverberation tail is obtained using an IIR lter network. The parameters dening this structure are automatically derived from the analyzed impulse response, using a minimization criteria based on Simultaneous Perturbation Stochastic Approximation (SPSA). The effectiveness of the proposed approach has been proved taking into account a real Italian Theatre impulse response providing comparison with the existing state-of-art techniques in terms of objective and subjective measures.
The document discusses adaptive filters, which can automatically adjust their parameters to filter signals whose exact frequency response is unknown. It defines adaptive filters as having an input signal, filter structure, adjustable parameters, and adaptive algorithm. The goal of adaptive filtering is to minimize the error between the filter's output and a desired response. It describes common adaptive filtering problems and solutions like using gradient descent algorithms and the mean squared error cost function to adjust the filter parameters over time and minimize error.
Acoustic echo cancellation using nlms adaptive algorithm ranbeerRanbeer Tyagi
The document discusses acoustic echo cancellation using the NLMS adaptive algorithm. It introduces the acoustic echo problem in hands-free communication systems and how echo cancellation works by using an adaptive filter to generate an echo replica that is subtracted from the echo signal. It then describes the NLMS adaptive algorithm and how it offers improved convergence over LMS with low computational complexity. Simulation results show NLMS effectively cancels echo. Future work topics are enhancing performance in noisy and double-talk conditions.
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cance...CSCJournals
We have designed and simulated an acoustic echo cancellation system for conferencing. This system is based upon a least-mean-square (LMS) adaptive algorithm and uses multi filter technique. A comparative study of both structure has been carried out and it is found that this new multi-filter converge faster than similar single long adaptive filter. Index Terms: LMS,Multiple sub filter ,Echo cancellation
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). It presents the following:
1. Fullband adaptive filters can have slow convergence due to correlated speech input and long echo path impulse responses. Subband adaptive filters (SAFs) address this by using individual adaptive filters in spectral subbands.
2. Adaptive combination of SAFs provides a way to achieve both fast convergence and small steady-state error. It independently adapts filters with different step sizes, then combines them using a mixing parameter adapted by stochastic gradient descent.
3. The proposed method adaptively combines NSAFs in subbands. It uses a large step size filter for fast convergence and a
FOLDED ARCHITECTURE FOR NON CANONICAL LEAST MEAN SQUARE ADAPTIVE DIGITAL FILT...VLSICS Design
Power consumption reduction is transpiring drift in area of VLSI digital signal processing. This gives rise
to need of minimization of silicon area which is done by folding algorithm. As silicon area decreases power
consumption of a circuit decreases. Folding is an algorithm which reduces silicon chip area by combining
various arithmetic operations into one operation by time scheduling technique. It is applied on iterative
data flow graph with appropriate folding set. Least mean square algorithm alters coefficients of Adaptive
filter in order to achieve desired output. Proposed work is focused on design of efficient VLSI architecture
for LMS adaptive filter aims at reducing mainly area which results in power consumption reduction and
hardware complexity. LMS filter structure used here is called non-canonical as transpose FIR structure is
used. Results show that numbers of adders are reduced by 37.5 % and multipliers by 33.33% without
changing characteristics of filter.
Asymmetric recursive Gaussian filtering for space-variant artificial bokehTuan Q. Pham
This document describes an asymmetric recursive Gaussian filter for space-variant artificial bokeh. The filter approximates two-dimensional space-variant blur using separable one-dimensional Gaussian filtering along the x- and y- dimensions. Within each dimension, the Gaussian filter is approximated by parallel forward and backward infinite impulse response (IIR) filters. The filter reduces intensity leakage at blur discontinuities by modifying the blur sigma of the IIR filters differently for the forward and backward passes as they approach discontinuities, resulting in an asymmetric space-variant filter. This asymmetric recursive filter is able to produce visually pleasing background blur for scenes with contents at different depths without smearing artifacts.
This document discusses filter banks, which are arrays of bandpass filters that separate an input signal into multiple sub-band components. It covers types of filter banks like analysis and synthesis banks, as well as uniform and non-uniform filter banks. Two-channel and polyphase two-channel filter banks are explained in more detail. Applications like signal compression and graphic equalizers are also mentioned. Lifting approaches for implementing filters efficiently are briefly outlined.
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.
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
This document discusses and compares various adaptive filtering algorithms for noise cancellation, including LMS, NLMS, RLS, and APA. It finds that RLS converges the fastest but has the highest complexity, while LMS converges the slowest but is simplest. NLMS and APA provide a balance between convergence speed and complexity. The document implements these algorithms on a noise cancellation problem and finds that RLS achieves the highest SNR improvement and best noise cancellation, followed by APA, NLMS, and LMS.
M.Tech Thesis on Simulation and Hardware Implementation of NLMS algorithm on ...Raj Kumar Thenua
This document is a dissertation submitted by Raj Kumar Thenua for the degree of Master of Technology in electronics and communication engineering from Sobhasaria Engineering College in Sikar, India. The dissertation investigates simulation and hardware implementation of the NLMS adaptive filtering algorithm on a TMS320C6713 digital signal processor. It includes an introduction, literature review, overview of adaptive filtering algorithms like LMS, NLMS and RLS, MATLAB simulation of these algorithms, Simulink model design for hardware implementation, real-time implementation on the TMS320C6713 processor and results and discussion.
Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering
The document summarizes speech recognition front-end technologies including voice activity detection (VAD) and speech enhancement. It discusses conventional signal processing based approaches and more recent deep learning based methods. For VAD, it describes adaptive context attention models that can dynamically adjust the context used based on noise type and SNR. For speech enhancement, it proposes a two-step neural network approach consisting of a prior network that makes multiple predictions from noisy features and a post network that combines these using a boosting method to produce enhanced features, allowing end-to-end training without an explicit masking step. The approach aims to better exploit neural network modeling power while reducing computation cost compared to conventional methods or single-step deep learning frameworks.
The document discusses the discrete cosine transform (DCT) and compares it to the discrete Fourier transform (DFT). Some key points:
- DCT provides better energy compaction than DFT, making it useful for image/signal compression applications. It transforms a signal from the spatial domain to the frequency domain.
- DCT is a real-valued transform derived from DFT, but is more computationally efficient as it avoids redundant complex calculations for real input data.
- The one-dimensional and two-dimensional DCT transforms are defined. DCT has properties like separability and invertibility like DFT.
- DCT coefficients represent the signal's frequency content, with low frequencies concentrated in
This document summarizes a research paper that compares different digital filtering techniques for removing noise from electrocardiogram (ECG) signals. It describes how finite impulse response (FIR) filters were designed using various windowing techniques, including rectangular, Hamming, Hanning, and Blackman windows. Infinite impulse response (IIR) filters and wavelet transforms were also evaluated for denoising ECG signals. The performance of the different filtering approaches were compared based on the power spectral density and average power of the signals before and after filtering. The paper found that an FIR filter designed with the Kaiser window showed the best results for noise removal from ECG signals.
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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.
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.
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Numerous technologies have been deployed to assist and manage transportation .But recent concerted efforts in academia andindustry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms,and will have enhanced sensing capabilities .They will enable new versatile systemsthat enhance transportation efficiency. This article surveys the sate-of-art approaches towards the future outlook on intelligent transportation. Current capabilities as well as limitations and opportunities of key enabling technologies are reviewed along with details of numerous notable projects that have been done around the world. Finally report also reviews the legal and regulatory uncertainties.
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.
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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.
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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.
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Real-time DSP Implementation of Audio Crosstalk Cancellation using Mixed Unif...CSCJournals
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International Journal of Engineering and Science Invention (IJESI)inventionjournals
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Design and implementation of different audio restoration techniques for audio...eSAT Journals
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Performance Analysis of Acoustic Echo Cancellation Techniques
1. Rajeshwar Dass Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.172-180
www.ijera.com 172 | P a g e
Performance Analysis of Acoustic Echo Cancellation Techniques Rajeshwar Dass1, Sandeep2 1,2(Department of ECE, D.C.R. University of Science &Technology, Murthal, Sonepat (HR), India) ABSTRACT Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applications. So, there exists an alternative solution i.e. to implement the filters in frequency domain. AEC has so many applications in wide variety of problems in industrial operations, manufacturing and consumer products. Here in this paper, a comparative analysis of different acoustic echo cancellation techniques i.e. Frequency domain adaptive filter (FDAF), Least mean square (LMS), Normalized least mean square (NLMS) &Sign error (SE) is presented. The results are compared with different values of step sizes and the performance of these techniques is measured in terms of Error rate loss enhancement (ERLE), Mean square error (MSE)& Peak signal to noise ratio (PSNR).
Keywords–Acoustic echo cancellation, Error rate loss enhancement, Frequency domain adaptive filtering, Least mean square, Mean square error, Normalized least mean square, Peak signal to noise ratio, Sign error.
I. INTRODUCTION
The term echo cancellation is basically used in a telephony system for describing the process of removing echo from a voice communication system. The history of echo cancellation started from 10th July 1962[1]. Echo cancellation is basically required to improve the call quality, to provide enhanced network performance, for excellent tone rejection, to maintain customer reliability & to provide excellent ERLE performance [1]. There are some challenges which should be kept in mind at the time of designing AEC techniques i.e. circuit complexity, selection of filter length, selection of suitable step size, in finding echo path, different types of noises (acoustic, thermal, DSP related noise) present in circuit, convergence time/speed, computational cost, number of required iterations, computational complexity, residual error and sampling rate [1]. In a telecommunication system, echo can degrades the quality of service. Echo is the repetition of a waveform due to reflection from points where the characteristics of the medium through which the wave propagates changes. In a communication system, echo is generally undesirable but unavoidable [4]. So echo cancellation is an important part of communication system. In an AEC system, as shown in fig.1, a measured microphone signal (d(n)) contains two signals: - the far-end echoed speech signal (x(n)) and the near-end speech signal (v(n)). The aim is to remove the far-end echoed speech signal from the microphone signal using different adaptive filter algorithms, sothat only the near-end speech signal is transmitted. An adaptive filter is a digital filter which adjusts its coefficients to provide the best match to a given desired signal [8].
Fig.1. General Configuration of An Adaptive Echo Cancellor
The remainder of the paper is described as follow: Section 1 briefly describes the basic introduction about echo cancellation. Section 2 describes different types of algorithms/ techniques which are used for acoustic echo cancellation. In section 3, the various performance analysis parameters are presented. In Section 4, the results of
Adaptive Echo Canceller
Echo Path
Σ
Σ
Echo
Far-end echoed speech signal x(n)
Near- end speech signal v(n)
Microphone signal d(n)
Error signal e(n)
푑 (푛)
+
+
+
-
RESEARCH ARTICLE OPEN ACCESS
2. Rajeshwar Dass Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.172-180
www.ijera.com 173 | P a g e
different echo cancellation techniques are presented & in section 5, the conclusion and future scope is discussed.
II. AEC TECHNIQUES
In this section, some of the techniques are described which are used for the echo cancellation and these are as follow:
i. FIR frequency domain adaptive filter
The frequency domain adaptive filter used in AEC is very beneficial when the impulse response of the system to be identified is long[2]. In order to compute the output signal and filter updates, this filter uses a fast convolution technique. The main advantage of this kind of filter is that they have improved convergence performance through frequency-bin step size normalization and quickly executes in MATLAB[3]. Generally, the adaptive filters are implemented in time domain which works efficiently for most of the applications[4]. But in many of the applications the impulse response of the filter becomes too large, which increases the complexity of the filter beyond a certain level where it can be no longer implemented effectively in time domain i.e. in AECapplications [5].That is why the filter is implemented in frequency domain. Mainly the Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) used for the conversion of signals from time domain to frequency domain[6]. The frequency domain weight vector is defined as[3], W(k) = [푊0 푘 ,……,푊푀−1 푘 ]푇 (1) And the input signal matrix is given as[3], X(k) = diag{푋0(k),……,푋푀−1(푘)}(2) Where diag{.} is an operator which generates a diagonal matrix. The number of elements, M, depends on FDAF configuration (usually M = N or M = 2N). The frequency-domain output vector can be given as the following matrix/vector product[3]: Y(k) = X(k)W(k) (3) and the weight update equation is given as[3], W(k+1) = W(k) + 2Gμ(k)푋퐻(푘)퐸(푘)(4) where superscript H denotes complex conjugate transpose,μ(k) represents the time varying diagonal matrix which contains step sizes,matrix G shows a constraint on gradient푋퐻(푘)퐸(푘)&E(k) is the fourier transform of error matrix e(n), which is computed as, e(n) = d(n) - 푑 (n) This technique has very low computational complexity, faster convergence and consumes less processing power at the same time.
ii. FIR frequency domain adaptive filter with LMS algorithmq
The LMS method is initially proposed by widrow Hoff in 1959[7]. This algorithm adapts to a solution of minimizing mean-square error. This method is based on steepest-descent method. In this, the gradient of mean-squared error is find out with respect to h. If w(n) is the weight vector and x(n) is the input signal of adaptive filter then, output y(n) of the adaptive filter is given by[14] y(n) = 푤(푛)푇x(n) (5) and the error signal e(n) is given by[14], e(n) = d(n)-y(n) (6) and the weight update equation is given by[14], w(n+1) = w(n) + μe(n)x(n) (7) where μ is the step size which controls the convergence rate. If the value of μ is small, then the convergence time is more. So the selection of suitable value of step size is very important[3],[8]. This algorithm is very simple and only requires few numbers of additions and multiplications per iteration for an N-tap filter. It has low computational complexity and the problem of double-talk is removed. But this method takes a fixed value of step size for every iteration [9].
iii. FIR frequency domain adaptive filter with NLMS algorithm
Basically, this algorithm is an extension of LMS. This method achieves faster convergence in time- domain as compared to frequency domain. Also, it has less complexity than LMS algorithm[10],[11]. It uses the weight updation equation as[14], 푤(푛+1) = 푤푛 + μ x(n) x(n) 2e(n) (8) where μ is step size. Here, with the normalization of step size by 푥(푛) 2, the noise amplification problem is diminished but problem occurs when 푥(푛) becomes too small. Therefore, the NLMS algorithm is modified as[14], w(n+1) = wn + μ x(n) ϵ+ x(n) 2e(n) (9) whereퟄ is a small positive number. This converges faster than LMS algorithm because it uses time varying step size calculation, but its computational complexity is high.
iv. FIR frequency domain adaptive filter with Sign-Error algorithm
Here, the sign-error algorithm is used with frequency domain adaptive filter. It is same as the LMS approach with the difference of different convergence time & computational complexity[12],[13]. Also this method provides better results for high value of filter length & for small value of step sizes.
III. PERFORMANCE ANALYSIS PARAMETERS
The various performance parameters which are used here for the performance evaluation of different AEC methods are as follow:
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i. ERLE (Error rate loss enhancement): It is a smoothed measure of the amount (in dB) that the echo has been attenuated. The formula which is used for ERLE is given by[9],
ERLE = 10log10 퐸[푑2 푛 ] 퐸[푒2 푛 ] (10) where d(n) is the far-end echoed signal and e(n) is the residual echo after cancellation.
ii. Mean square error: It contains the sequence of mean-square errors. This column vector contains predictions of the mean-square error of adaptive filter at each time instant.The MSE is calculated as[5],
MSE = 1 푁 푒(푘)2푁푘 =1(11) where N is the filter length and e(k) is the error signal achieved at the output of filter.
iii. Peak signal to noise ratio (PSNR): PSNR computes the peak signal-to-noise ratio, between two signals. This ratio is often used as a quality measurement between the original and the output signal. The higher the PSNR, the better the quality of output signals.The PSNR is calculated as,
PSNR = 10*log10((R*R)/MSE) (12) whereMSE is the value of mean square error and R is the maximum fluctuation in the input signal.
IV. RESULT & DISCUSSIONS
Firstly, the acoustics of the loudspeaker-to- microphone signal path are described where the speakerphone is located. As shown in fig.2(a), a long finite impulse response filter is used to describe these characteristics which generates a random impulse response at a sampling rate of fs = 8000 Hz. The teleconferencing system’s user is mainly located near the system’s microphone which is called as near end speech signal as shown in fig. 2(b). A voice travels out the loudspeaker, bounces around in the room, and this voice is picked up by the system’s microphone; this voice signal is called as far end speech signalas shown in fig.2(c). Also in fig.2(d), the microphone signal contains both the near end speech and the far end speech that has been echoed throughout the room is shown.
Fig.2: (a).Impulse response of room, (b).Near-end speech signal, (c).Far-end echoed speech signal, (d).Microphone signal Output of acoustic echo canceller is observed by using frequency domain adaptive filtering method as shown in fig.3 to fig.6.Here, the value of filter length (N) is varied and the value of step size (μ) is taken as constant i.e. 0.025. The goal of adaptive echo canceller is to remove the far end echoed speech signal, so that only near end speech signal is transmitted back to the far-end listener. Since, we have access to both near end and far end speech signals, so echo return loss enhancement is also calculated, which is the amount (in dB) that how much echo has been attenuated. From fig.6 it has been seen that approx. 30 dB ERLE is achieved at the end of convergence period using FDAF algorithm.
Fig.3: (a).Near-end speech signal, (b).Microphone signal, (c).Output of FDAF algorithm when filter length, N = 32 & μ = 0.025, (d).Echo return loss enhancement
In all the figures from fig.3 to fig.18, (a)&(b) part shows the near-end speech signal and the microphone speech signalrespectively. In fig. 3(c),
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the output of FDAF algorithm is shown when the filter length is 32 & step size is 0.025. In fig.3(d), the Amount of ERLE achieved is shown i.e approx. 1 dB. Also in fig. 4(c), the output of FDAF algorithm is shown when the filter length is 128& step size is 0.025. In fig.4(d), the Amount of ERLE achieved is shown i.e approx. 8 dB.
Fig.4: (a).Near-end speech signal, (b).Microphone signal, (c).Output of FDAF algorithm when filter length, N = 128& μ = 0.025, (d).Echo return loss enhancement In fig. 5(c), the output of FDAF algorithm is shown when the filter length is 512& step size is 0.025. In fig.5(d), the Amount of ERLE achieved is shown i.e approx. 20 dB.
Fig.5: (a).Near-end speech signal, (b).Microphone signal, (c).Output of FDAF algorithm when filter length, N = 512& μ = 0.025, (d).Echo return loss enhancement In fig. 6(c), the output of FDAF algorithm is shown when the filter length is 2048& step size is 0.025. In fig.6(d), the Amount of ERLE achieved is shown i.e approx. 30 dB.
Fig.6: (a).Near-end speech signal, (b).Microphone signal, (c).Output of FDAF algorithm when filter length, N = 2048& μ = 0.025, (d).Echo return loss enhancement Table 1shows the ERLE achieved for different values of step sizes and filter lengths for the FDAF algorithm. It is clear from the results of table that if the value of filter length is constant & the value of step size is increases, then the amount of ERLE achieved at the end of convergence period is decreases. So, FDAF algorithm works better for the filter length of 2048 and step size of 0.025. Now, the output of acoustic echo canceller is observed by using frequency domain adaptive filter which uses LMS algorithm as shown in fig.7 to fig.10. Here, the value of filter length (N) is varied and the value of step size (μ) is taken as constant i.e. 0.07 for achieving best results. In fig. 7(c), the output of LMS algorithm is shown when the filter length is 32 & step size is 0.07. In fig.7(d), the Amount of ERLE achieved is shown i.e approx. 4 dB.
Fig.7: (a).Near-end speech signal, (b).Microphone signal, (c).Output of LMS algorithm when filter length, N = 32 & μ = 0.07, (d).Echo return loss enhancement
Also in fig. 8(c), the output of LMS algorithm is shown when the filter length is 128& step size is
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0.07. In fig.8(d), the Amount of ERLE achieved is shown i.e approx.5 dB.
Fig.8: (a).Near-end speech signal, (b).Microphone signal, (c).Output of LMS algorithm when filter length, N = 128 & μ = 0.07, (d).Echo return loss enhancement In fig. 9(c), the output of LMS algorithm is shown when the filter length is 512& step size is 0.07. In fig.9(d), the Amount of ERLE achieved is shown i.e approx. 15 dB.
Fig.9: (a).Near-end speech signal, (b).Microphone signal, (c).Output of LMS algorithm when filter length, N = 512 & μ = 0.07, (d).Echo return loss enhancement In fig. 10(c), the output of LMS algorithm is shown when the filter length is 2048& step size is 0.07. In fig.9(d), the Amount of ERLE achieved is shown i.e approx. 20 dB.
Fig.10: (a).Near-end speech signal, (b).Microphone signal, (c).Output of LMS algorithm when filter length, N = 2048& μ = 0.07, (d).Echo return loss enhancement
Table 2 shows the ERLE achieved for different values of step sizes and filter lengths for the LMS algorithm. It is clear from the results of table that if the value of filter length is constant & the value of step size is increases, then the amount of ERLE achieved at the end of convergence period is firstly increases, then decreases. So, LMS algorithm works better for the filter length of 2048 and step size of 0.07. Now, the output of acoustic echo canceller is observed by using frequency domain adaptive filter which uses NLMS algorithm as shown in fig.11 to fig.14. Here, the value of filter length (N) is varied and the value of step size (μ) is taken as constant i.e. 0.1 for achieving best results. In fig. 11(c), the output of NLMS algorithm is shown when the filter length is 32 & step size is 0.1. In fig.11(d), the Amount of ERLE achieved is shown i.e approx. 0.2 dB.
Fig.11:(a).Near-end speech signal, (b).Microphone signal, (c).Output of NLMS algorithm when filter length, N = 32 & μ = 0.1, (d).Echo return loss enhancement Also in fig. 12(c), the output of NLMS algorithm is shown when the filter length is 128& step size is 0.1. In fig.12(d), the Amount of ERLE achieved is shown i.e approx.1 dB.
Fig.12: (a).Near-end speech signal, (b).Microphone signal, (c).Output of NLMS algorithm when filter length, N = 128& μ = 0.1, (d).Echo return loss enhancement In fig. 13(c), the output of NLMS algorithm is shown when the filter length is 512& step size is 0.1. In fig.13(d), the Amount of ERLE achieved is shown i.e approx.2 dB.
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Fig.13: (a).Near-end speech signal, (b).Microphone signal, (c).Output of NLMS algorithm when filter length, N = 512& μ = 0.1, (d).Echo return loss enhancement In fig. 14(c), the output of NLMS algorithm is shown when the filter length is 2048& step size is 0.1. In fig.14(d), the Amount of ERLE achieved is shown i.e approx.2 dB.
Fig.14: (a).Near-end speech signal, (b).Microphone signal, (c).Output of NLMS algorithm when filter length, N = 2048& μ = 0.1, (d).Echo return loss enhancement Table 3 shows the ERLE achieved for different values of step sizes and filter lengths for the NLMS algorithm. It is clear from the results of table that if the value of filter length is constant & the value of step size is increases, then the amount of ERLE achieved at the end of convergence period is increases. So, the results shows that NLMS algorithm does not provides better results for the assuming range of filter length and step size. Now, the output of acoustic echo canceller is observed by using frequency domain adaptive filter which uses SE algorithm as shown in fig.15 to fig.18. Here, the value of filter length (N) is varied and the value of step size (μ) is taken as constant i.e. 0.025 for achieving best results. In fig. 15(c), the output of SE algorithm is shown when the filter length is 32 & step size is 0.025. In fig.15(d), the Amount of ERLE achieved is shown i.e approx.2 dB.
Fig.15: (a).Near-end speech signal, (b).Microphone signal, (c).Output of SE algorithm when filter length, N = 32& μ = 0.025, (d).Echo return loss enhancement In fig. 16(c), the output of SE algorithm is shown when the filter length is 128& step size is 0.025. In fig.16(d), the Amount of ERLE achieved is shown i.e approx.8 dB.
Fig.16: (a).Near-end speech signal, (b).Microphone signal, (c).Output of SE algorithm when filter length, N = 128& μ = 0.025, (d).Echo return loss enhancement In fig. 17(c), the output of SE algorithm is shown when the filter length is 512& step size is 0.025. In fig.17(d), the Amount of ERLE achieved is shown i.e approx.14 dB.
Fig.17: (a).Near-end speech signal, (b).Microphone signal, (c).Output of SE algorithm when filter length, N = 512& μ = 0.025, (d).Echo return loss enhancement
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Parameters
FDAF ALGORITHM
Filter length
N = 32
N = 128
N = 512
N = 2048
Step size (μ)
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
ERLE (dB)
1
1
1
8
5
5
20
12
12
30
20
18
Table no.1.ERLE for different filter length & step sizes for FDAF algorithm
Parameters
LMS ALGORITHM
Filter length
N = 32
N = 128
N = 512
N = 2048
Step size (μ)
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
ERLE (dB)
3
4
2
6
5
4
8
15
10
10
20
15
Table no.2.ERLE for different filter length & step sizes for LMS algorithm
Parameters
NLMS ALGORITHM
Filter length
N = 32
N = 128
N = 512
N = 2048
Step size (μ)
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
ERLE (dB)
0.1
0.1
0.2
0.2
0.5
1
0.5
0.5
2
1
2
2
Table no.3.ERLE for different filter length & step sizes for NLMS algorithm
Parameters
SE ALGORITHM
Filter length
N = 32
N = 128
N = 512
N = 2048
Step size (μ)
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
0.025
0.07
0.1
ERLE (dB)
2
2
2
8
5
8
14
10
8
15
12
5
Table no.4.ERLE for different filter length & step sizes for SE algorithm
Algorithms→ Paramaters ↓
LMS
NLMS
SE
Step size(μ)
0.025
0.7
0.1
0.025
0.7
0.1
0.025
0.7
0.1
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ERLE(dB)
3
4
2
0.1
0.1
0.2
2
2
2
MSE
0.0070
0.0070
0.0067
0.0077
0.0078
0.0070
0.0074
0.0071
0.0064
PSNR(dB)
21.5653
21.5323
21.7142
21.1190
21.0599
21.5718
21.3193
21.4668
21.9349
Table no.5 (when filter length N = 32)
Algorithms→ Paramaters ↓
LMS
NLMS
SE
Step size(μ)
0.025
0.7
0.1
0.025
0.7
0.1
0.025
0.7
0.1
ERLE(dB)
6
5
4
0.2
0.5
1
8
5
8
MSE
0.0062
0.0061
0.0064
0.0064
0.0061
0.0062
0.0061
0.0061
0.0058
PSNR(dB)
22.0921
22.1718
21.9136
21.9495
22.1387
22.0921
22.1718
22.1235
22.3347
Table no.6 (when filter length N = 128)
In fig. 18(c), the output of SE algorithm is shown when the filter length is 2048 & step size is 0.025. In fig.18(d), the Amount of ERLE achieved is shown i.e approx. 15 dB.
Fig.18: (a).Near-end speech signal, (b).Microphone signal, (c).Output of SE algorithm when filter length, N = 2048 & μ = 0.025, (d).Echo return loss enhancement Table 4 shows the ERLE achieved for different values of step sizes and filter lengths for the SE algorithm. It is clear from the results of table that if the value of filter length is constant & the value of step size is increases, then the amount of ERLE achieved at the end of convergence period is mostly decreases. So, SE algorithm provides better results for the filter length of 2048 and step size of 0.025. Also the performance analysis of these algorithms is done by calculating ERLE, MSE & PSNR by using different values of step sizes and filter length, which is shown in table no.5 & 6. It is also clear from table no.5 that the LMS algorithm provides better results i.e. ERLE = 4 dB, MSE = 0.070 & PSNR = 21.5323 for the filter length of 32 and step size of 0.7. Also if the value of step size is increases, the value of MSE decreases and the value of PSNR increases. It is also clear from table no.6 that the SE algorithm provides better results i.e. ERLE = 8 dB, MSE = 0.058 & PSNR = 22.3347 for the filter length of 128 and step size of 0.1. Also if the value of step size is increases, the value of MSE decreases and the value of PSNR increases.
V. CONCLUSION AND FUTURE SCOPE
From the above tables & results it is clear that for the step size of 0.025 & filter length of 2048, the FDAF algorithm provides better results i.e. the ERLE of 30 dB. Similarly, the LMS algorithm works better for the step size of 0.07 and filter length of 2048. But if the value of step size is increases up to 0.3, then approx. 35 dB ERLE is achieved. The NLMS algorithm does not provide better results for the given range of step size &filter length. Similarly, the SE algorithm provides good results for the step size of 0.025 & filter length of 2048. Also, by studying all these algorithms regarding echo cancellation, the use of better performance algorithm and different filter structure for the elimination of echo signal would be the future research. Each algorithm has some operational limitations, but a reliable system can be developed using suitable algorithm for echo removal.
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REFERENCES
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