In this paper we propose a new method of processing speech degraded by reverberation. The method is based on analysis of short (2 ms) segments of data to enhance the regions in the speech signal having high signal to reverberant component ratio (SRR). The short segment analysis shows that SRR is different in different segments of speech. The processing method involves identifying and manipulating the linear prediction residual in three different regions of the speech signal, namely, the high SRR region, the low SRR region and only the reverberation component region. A weighting function is derived to modify the LP residual. The weighted residual samples are used to excite the time-varying LP all-pole filter to obtain perceptually enhanced speech. © 1998 IEEE.

P. Satyanarayana Murthy

REVERBERATION

signal processing

Acoustic noise

acoustic signal processing

Speech analysis

ALL-POLE FILTERS

HIGH SIGNALS

LINEAR PREDICTION

METHOD OF PROCESSING

Processing method

SHORT SEGMENTS

Speech signals

Weighted residuals

Speech

Speech processing

REVERBERANT SPEECH

SIGNAL TO REVERBERANT COMPONENT RATIO

speech enhancement

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Enhancement of reverberant speech using LP residual

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

In this paper, we propose a new method of processing speech degraded by reverberation. The method is based on analysis of short (2 ins) segments of data to enhance the regions in the speech signal having high signal-to-reverberant component ratio (SRR). The short segment analysis shows that SRR is different in different segments of speech. The processing method involves identifying and manipulating the linear prediction residual signal in three different regions of the speech signal, namely, high SRR region, low SRR region, and only reverberation component region. A weight function is derived to modify the linear prediction residual signal. The weighted residual signal samples are used to excite a time-varying all-pole filter to obtain perceptually enhanced speech. The method is robust to noise present in the recorded speech signal. The performance is illustrated through spectrograms, subjective and objective evaluations. © 2000 IEEE.

Fulltext

IEEE Transactions on Speech and Audio Processing

Enhancement of reverberant speech using LP residual signal

In this paper we propose a method for enhancement of speech in the presence of additive noise. The objective is to selectively enhance the high signal-to-noise ratio (SNR) regions in the noisy speech in the temporal and spectral domains, without causing significant distortion in the resulting enhanced speech. This is proposed to be done at three different levels. (a) At the gross level, by identifying the regions of speech and noise in the temporal domain. (b) At the finer level, by identifying the regions of high and low SNR portions in the noisy speech. (c) At the short-time spectrum level, by enhancing the spectral peaks over spectral valleys. The basis for the proposed approach is to analyze linear prediction (LP) residual signal in short (1-2 ms) segments to determine whether a segment belongs to a noise region or speech region. In the speech regions the inverse spectral flatness factor is significantly higher than in the noisy regions. The LP residual signal enables us to deal with short segments of data due to uncorrelatedness of the samples. Processing of noisy speech for enhancement involves mostly weighting the LP residual signal samples. The weighted residual signal samples are used to excite the time-varying all-pole filter to produce enhanced speech. As the additive noise level in the speech signal is increased, the quality of the resulting enhanced speech decreases progressively due to loss of speech information in the low SNR, high noise regions. Thus the degradation in performance of enhancement is graceful as the overall SNR of the noisy speech is decreased.

Speech Communication

Speech enhancement using linear prediction residual

Cepstrum-based deconvolution for speech dereverberation

Proceedings of the IEEE

Linear prediction: A tutorial review

Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96

Study on the dereverberation of speech based on temporal envelope filtering

Journal	Data powered by TypesetICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN	15206149
Open Access	No