Adaptive speech enhancement using frequency-specific SNR estimates

Avendaño, Carlos; Heřmanský, Hynek; Vis, M.; Bayya, Aruna

doi:10.1109/ivtta.1996.552760

Cited by 11 publications

(4 citation statements)

References 1 publication

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“…This approach resulted in better speech quality with lower phase distortion as compared to their previous work [69]. Avendano et al followed up this approach by incorporating the local SNRs into their FIR filter design [87]. This work was motivated by the observation made in [85] that the characteristics of the filter were related to the local SNRs for each frequency subband.…”

Section: Filter Bank Based Methodsmentioning

confidence: 99%

Speech enhancement in modulation domain using codebook-based speech and noise estimation

Mani

Champagne

Zhu

2015

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Conventional single-channel speech enhancement methods implement the analysis-modification-synthesis (AMS) framework in the acoustic frequency domain. Recently, it has been shown that the extension of this framework to the modulation domain may result in better noise suppression. However, this conclusion has been reached by relying on a minimum statistics approach for the required noise power spectral density (PSD) estimation.Various noise estimation algorithms have been proposed over the years in the speech and audio processing literature. Among these, the widely used minimum statistics approach is known to introduce a time frame lag in the estimated noise spectrum. This can lead to highly inaccurate PSD estimates when the noise behaviour rapidly changes with time, i.e., non-stationary noise. Speech enhancement methods which employ these inaccurate noise PSD estimates tend to perform poorly in the noise suppression task, and in worst cases, may end up deteriorating the noisy speech signal even further. Noise PSD estimation algorithms using a priori information about the noise statistics have been shown to track non-stationary noise better than the conventional algorithms which rely on the minimum statistics approach.In this thesis, we perform noise suppression in the modulation domain with the noise and speech PSD derived from an estimation scheme which employs the a priori information of various speech and noise types. Specifically, codebooks of gain normalized linear prediction coefficients obtained from training on various speech and noise files are used as the a priori information while performing the estimation of the desired PSD. The PSD estimates derived from this codebook approach are used to obtain a minimum mean square error (MMSE) estimate of the clean speech modulation magnitude spectrum, which is then combined with the phase spectrum of the noisy speech to recover the enhanced speech signal. The enhanced speech signal is subjected to various objective experiments for evaluation. Results of these evaluations indicate improvement in noise suppression with the proposed codebook-based modulation domain approach over competing approaches, particularly in cases of nonstationary noise.ii Sommaire Les méthodes conventionnelles de rehaussement de la paroleà canal unique utilisent une structure d'analyse-modification-synthèse (AMS) dans le domaine fréquentiel. Récemment, il aété démontré que l'utilisation de cette structure dans le domaine de la modulation pourrait offrir une meilleure suppression du bruit. Toutefois, cette conclusion aété obtenue en se basant sur une approcheà statistique minimale pour l'estimation de la densité spectrale de puissance (PSD) du bruit ambiant.Plusieurs algorithmes d'estimation de bruit ontété proposés au fil des ans dans la littérature sur le traitement de la parole. D'ordinaire, les méthodes d'estimation de bruit qui se servent d'une approcheà statistique minimale vont créer un décalage temporel dans l'estimation spectrale du bruit. Ce décalage peut engendrer beaucou...

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Section: Filter Bank Based Methodsmentioning

confidence: 99%

Speech enhancement in modulation domain using codebook-based speech and noise estimation

Mani

Champagne

Zhu

2015

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…As noted previously, speech is mostly constrained between 2 and 20 Hz f mod , and researchers first proposed the use of low/bandpass filters in the modulation spectrum domain for speech enhancement (e.g., [64,65]). The idea was later applied for environmentrobust feature extraction via the so-called RASTA (relative spectral) feature processing pipeline [66].…”

Section: Speech Signalsmentioning

confidence: 99%

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

Tiwari

Cassani

Kshirsagar

et al. 2022

Sensors

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Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.

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“…Good results were obtained using time frames of 500ms. This technique was adopted by Avendano et al (1996) and applied to speech enhancement. Pre-computed FIR filters were selected in each sub-band depending on the respective estimated sub-band SNR in order to increase the SNR.…”

Section: Introductionmentioning

confidence: 99%

Sub-band SNR estimation using auditory feature processing

Kleinschmidt

Hohmann

2003

Speech Communication

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In this paper a new approach is presented for estimating the long-term speechto-noise ratio (SNR) in individual frequency bands that is based on methods known from automatic speech recognition (ASR). It uses a model of auditory perception as front end, physiologically and psychoacoustically motivated sigma-pi cells as secondary features, and a linear or non-linear neural network as classifier. A non-linear neural network back end is capable of estimating the SNR in time segments of 1s with a root-mean-square error of 5.68dB on unknown test material. This performance is obtained on a large set of natural types of noise, containing instationary signals and alarm sounds. However, the SNR estimation works best for more stationary types of noise. The individual components of the estimation algorithms are examined with respect to their importance for the estimation accuracy. The algorithm presented in this paper yields similar or better results with comparable computational effort relative to other methods known from the literature for shortterm SNR estimation. The new approach is purely based on slow spectro-temporal modulations and is therefore a valuable contribution to both, digital hearing aids and ASR systems. Zusammenfassung

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Adaptive speech enhancement using frequency-specific SNR estimates

Cited by 11 publications

References 1 publication

Speech enhancement in modulation domain using codebook-based speech and noise estimation

Speech enhancement in modulation domain using codebook-based speech and noise estimation

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

Sub-band SNR estimation using auditory feature processing

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