Conferences related to Speech Enhancement

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

The conference is the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of foundational research in automation among researchers, academics, and practitioners.


2019 IEEE International Symposium on Information Theory (ISIT)

Information theory and coding theory and their applications in communications and storage, data compression, wireless communications and networks, cryptography and security, information theory and statistics, detection and estimation, signal processing, big data analytics, pattern recognition and learning, compressive sensing and sparsity, complexity and computation theory, Shannon theory, quantum information and coding theory, emerging applications of information theory, information theory in biology.


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Periodicals related to Speech Enhancement

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


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Most published Xplore authors for Speech Enhancement

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Xplore Articles related to Speech Enhancement

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Advanced speech enhancement with partial speech reconstruction

21st European Signal Processing Conference (EUSIPCO 2013), 2013

An advanced speech enhancement algorithm is proposed, which employs partial speech reconstruction of highly disturbed speech. The speech reconstruction algorithms assume the source-filter model of speech production and construct estimates of clean speech source and filter signals using features extracted from noisy input. A nonlinear harmonic regeneration scheme for source signals is presented followed by two methods for the estimation ...


Dual Channel Coherence Based Speech Enhancement with Wavelet Denoising

2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018

The most essential work in a communication system is reconstructing a clean speech from a noisy environment which is a challenging problem in speech processing. The performance of speech processing system could increase by utilizing an effective speech enhancement technique in case of mobile phone, hearing aids, etc. An innovative speech enhancement technique may help in increasing the accuracy of ...


Speech enhancement based on combination of wiener filter and subspace filter

2014 International Conference on Audio, Language and Image Processing, 2014

This paper propose a novel multi-channel speech enhancement method by combining the wiener filtering and subspace filtering with a convex combinational coefficient. Because of using both the advantage in noise reduction of the subspace speech enhancement technology and the stable characteristic of the Wiener filtering technology, the proposed multi-channel speech enhancement method has a better performance in robustly removing colored ...


An Auditory-Based Monaural Feature for Noisy and Reverberant Speech Enhancement

2017 International Conference on Computing Intelligence and Information System (CIIS), 2017

The deep neural networks (DNN) based speech enhancements is a hot topic in machine learning and speech enhancement application. Even with deep neural network, it is still hard to improve the speech quality on noisy and reverberant conditions. For machine learning based system, auditory feature extraction becomes the key point in speech enhancement and recognition. In this paper, we proposed ...


The Application of Deep Neural Network in Speech Enhancement Processing

2018 5th International Conference on Information Science and Control Engineering (ICISCE), 2018

To solve the problem that Non-stationary noise is difficult to remove during speech enhancement process when using Fourier transform, this essay will put forward a speech enhancement algorithm based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and Deep Neural Network (DNN). Firstly, preprocessing the original signal by EEMD, and decomposing a series of time- frequency information of the ...


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Educational Resources on Speech Enhancement

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IEEE-USA E-Books

  • Advanced speech enhancement with partial speech reconstruction

    An advanced speech enhancement algorithm is proposed, which employs partial speech reconstruction of highly disturbed speech. The speech reconstruction algorithms assume the source-filter model of speech production and construct estimates of clean speech source and filter signals using features extracted from noisy input. A nonlinear harmonic regeneration scheme for source signals is presented followed by two methods for the estimation of the vocal tract filter characteristics. The quantization method applies a priori trained codebooks using clean speech training data and the parametric estimation method assumes a parabolic continuation of low frequency envelope values. The predicted speech quality of the enhanced speech output is assessed with composite objective measures, while the accuracy of the spectral envelope estimations is analyzed with the log-spectral distance over four manually generated signal-to-noise ratio scenarios.

  • Dual Channel Coherence Based Speech Enhancement with Wavelet Denoising

    The most essential work in a communication system is reconstructing a clean speech from a noisy environment which is a challenging problem in speech processing. The performance of speech processing system could increase by utilizing an effective speech enhancement technique in case of mobile phone, hearing aids, etc. An innovative speech enhancement technique may help in increasing the accuracy of speech recognition methods. In this paper a new approach has been introduced which deals with the implementation of dual channel speech enhancement technique utilizes the coherence function computed from the dual input signals without prior noise statistics for noise reduction. For post-processing, discrete wavelet transform (DWT) is used as a gain function which is set up by SNR values using coherence function. The proposed algorithm is applied in an environment where interfering speakers are present and gives a better result than other algorithms when the speech is corrupted by different noise types.

  • Speech enhancement based on combination of wiener filter and subspace filter

    This paper propose a novel multi-channel speech enhancement method by combining the wiener filtering and subspace filtering with a convex combinational coefficient. Because of using both the advantage in noise reduction of the subspace speech enhancement technology and the stable characteristic of the Wiener filtering technology, the proposed multi-channel speech enhancement method has a better performance in robustly removing colored noise from noisy speech signals. Simulation examples confirm that under different colored noise, the proposed multi-channel speech enhancement method can obtain better speech recovery results than the traditional subspace multi-channel speech enhancement method and the multi-channel Wiener filter speech enhancement method.

  • An Auditory-Based Monaural Feature for Noisy and Reverberant Speech Enhancement

    The deep neural networks (DNN) based speech enhancements is a hot topic in machine learning and speech enhancement application. Even with deep neural network, it is still hard to improve the speech quality on noisy and reverberant conditions. For machine learning based system, auditory feature extraction becomes the key point in speech enhancement and recognition. In this paper, we proposed a speech enhancement framework based on an auditory- based monaural feature, which model the function of human hearing auditory system. The auditory based feature is extracted from the data passing the gammatone filter banks, which has more detail on low frequency than normal filters. Systemic tests show the better performance of the proposed auditory based monaural feature than the mel-frequency cepstral coefficients (MFCC) in noise and reverberant environment.

  • The Application of Deep Neural Network in Speech Enhancement Processing

    To solve the problem that Non-stationary noise is difficult to remove during speech enhancement process when using Fourier transform, this essay will put forward a speech enhancement algorithm based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and Deep Neural Network (DNN). Firstly, preprocessing the original signal by EEMD, and decomposing a series of time- frequency information of the IMF component to meet the time-variation requirement better; Secondly, adjusting the weight of the IMF component by DNN and then synthesize it to enhanced the speech; Finally, comparing the differences of speech enhancement performance between using EEMD alone, using Fourier transform and EEMD as a preprocessing. The results show that the enhanced algorithm using EEMD as a preprocessing improves the scores of PESQ and STOI by 0.745 and 0.169 respectively, effectively improving the speech quality and intelligibility.

  • An adaptive algorithm using FIR filter for speech enhancement systems

    An adaptive algorithm for speech enhancement is studied in this paper. We propose the adaptive Lyapunov speech enhancement algorithm for nonlinear speech enhancement system using FIR filter. By using the Lyapunov stability theory, the filter coefficients can adaptively adjust so that the error converges to zero asymptotically. The adaptive algorithm ensures the speech enhancement system has a better performance than the traditional least mean square algorithm. For the case that the size, weight and power consumption, the design of a microphone array for hearing aids are serious hard to establish, the algorithm still work. At last, an example of speech enhancement system result demonstrated the feasibility and effectiveness of the proposed adaptive algorithm.

  • Exploring Tradeoffs in Models for Low-Latency Speech Enhancement

    We explore a variety of neural networks configurations for one- and two- channel spectrogram-mask-based speech enhancement. Our best model improves on previous state-of-the-art performance on the CHiME2 speech enhancement task by 0.4 decibels in signal-to-distortion ratio (SDR). We examine trade-offs such as non-causal look-ahead, computation, and parameter count versus enhancement performance and find that zero-look-ahead models can achieve, on average, within 0.03 dB SDR of our best bidirectional model. Further, we find that 200 milliseconds of look-ahead is sufficient to achieve equivalent performance to our best bidirectional model.

  • An 8.3mW 1.6Msamples/s multi-modal event-driven speech enhancement processor for robust speech recognition in smart glasses

    A low-power and high-speed speech enhancement processor for speech enhancement of noisy inputs is proposed to realize the robust speech recognition in smart glasses. It has 3 key schemes: multi-modal speech selection, look-up table based non-linear approximation circuits, and speech detection controlled dynamic clock gating. The multi-modal speech selection scheme uses three parameters to enhance the limited accuracy of the previous uni-modal user speech selection up to 98.1%. The non-linear function approximation circuit accelerates the throughput of the speech enhancement by 10.7×. The speech detection controlled clock gating reduces the redundant power consumption by 51% when there is no user voice. The proposed speech enhancement processor achieves 1.6Msamples/s throughput and 8.3mW average power consumption with the 98.1% true positive rate of speech selection in 65nm CMOS process.

  • Speech Enhancement Based on Multi-Stream Model

    In most of the current speech enhancement systems, speech signals collected by microphone are used as the only input data stream to recover the clean speeches, which will be greatly affected by the acoustic noise levels. Based on the fact that the noises or mismatches do not affect different data streams in similar ways, this paper proposes a new speech enhancement framework which can make use of multi-stream information even when some data streams are not directly related to the speech waveform by employing a multi-stream model based speech filter. A new speech enhancement method is also proposed based on the acoustic and throat microphone recordings. Experimental results show that the proposed method outperforms several conventional single stream speech enhancement methods in different noisy environments.

  • Binaural deep neural network for robust speech enhancement

    Robust speech enhancement is a challenge task, especially in noisy environments. The deep neural network has shown good performance on binaural speech enhancement with various speakers at a same distance. As binaural cues are based on the locations of sound sources, this paper analyze the performance of binaural deep neural network with different distances. The theoretical derivation and experiment shows, the computational auditory scene analysis based binaural deep neural network speech enhancement system has robust performance with various sound locations.



Standards related to Speech Enhancement

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No standards are currently tagged "Speech Enhancement"


Jobs related to Speech Enhancement

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