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Optimal Microphone Array Placement Design Using the Bayesian Optimization Method. [PDF]
Sensors (Basel)Zhang Y, Li Z, Yiu KFC.
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Effects of Reverberation on the Relation Between Compression Speed and Working Memory for Speech-in-Noise Perception. [PDF]
Ear Hear, 2019 Reinhart P, Zahorik P, Souza P.
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Speech Enhancement for Cochlear Implant Recipients using Deep Complex Convolution Transformer with Frequency Transformation. [PDF]
IEEE/ACM Trans Audio Speech Lang ProcessMamun N, Hansen JHL.
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An Overview of Speech Dereverberation
Lecture Notes in Electrical Engineering, 2021Speech dereverberation is an important preprocessing step in speech signal processing, aims at improving the sound quality by canceling or suppressing the effect of reverb. This paper provides an overview of speech dereverberation algorithms, showing the development of speech dereverberation technology.
Yuan Li, Lunhui Deng
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Regularization for Partial Multichannel Equalization for Speech Dereverberation
IEEE Transactions on Audio, Speech, and Language Processing, 2013 Acoustic multichannel equalization techniques such as the multiple-input/output inverse theorem (MINT), which aim to equalize the room impulse responses (RIRs) between the source and the microphone array, are known to be highly sensitive to RIR estimation errors.
Ina Kodrasi, Stefan Goetze, Simon Doclo
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Speech Dereverberation Enhancement [PDF]
, 2021 Reverberation is the process of multi-path propagation of a sound from its source to a receiver. It is a blind problem where the source signal is unknown and non-stationary as well as the acoustic channel also unknown and time-varying.
Othman O. Khalifa +2 more
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Dual microphone speech dereverberation
2007 9th International Symposium on Signal Processing and Its Applications, 2007This paper addresses the problem of intelligibility and quality improvement for the speech corrupted due to reverberation. Several speech de-reverberation algorithms (both single- and two-channel methods) are studied and compared. Based on this study, we propose some hybrid methods to improve both intelligibility and quality of reverberated speech. The
Hamid Reza Abutalebi, Farhad Faghani
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Bayesian learning for speech dereverberation
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016This study presents a Bayesian approach to enhance the magnitude spectra of single-channel reverberant speech signals. Speech dereverberation model is constructed by using a nonnegative convolutive transfer function (NCTF) and a nonnegative matrix factorization (NMF).
Jen-Tzung Chien, You-Cheng Chang
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Statistical models for speech dereverberation
2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2009This paper discusses a statistical-model-based approach to speech dereverberation. With this approach, we first define parametric statistical models of probability density functions (pdfs) for a clean speech signal and a room transmission channel, then estimate the model parameters, and finally recover the clean speech signal by using the pdfs with the
Takuya Yoshioka +3 more
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