Yu Gwang Jin

Spectro-Temporal Filtering for Multichannel Speech Enhancement in Short-Time Fourier Transform Domain

In this letter, we propose a spectro-temporal filtering algorithm for multichannel speech enhancement in the short-time Fourier transform (STFT) domain. Compared with the traditional multiplicative filtering technique, the proposed method takes account of interdependencies between components in adjacent frames and frequency bins. For spectro-temporal filtering, speech and noise power spectral density (PSD) matrices are estimated based on an extended formulation utilizing temporal and spectral correlations, and the parametric noise reduction filter based on these PSD matrices is applied to the input microphone array signal. Moreover, multichannel speech presence probabilities are also estimated within a unified framework. A number of experimental results show that the proposed spectro-temporal filtering method improves the performance of multichannel speech enhancement.

Quality Enhancement of Audio Watermarking for Data Transmission in Aerial Space Based on Segmental SNR Adjustment

Audio watermarking techniques can be used not only as a copyright protect system but also as a short-range wireless data transmission system between a loudspeaker and a microphone. To send data through aerial space using acoustic signal, the length of frequency analysis window should be longer than the reverberation time, which, however, can degrade the quality of watermarked audio signal. In this paper, we propose an audio quality enhancement technique for acoustic data transmission application based on segmental SNR adjustment. The acoustic data transmission system which employs the proposed audio watermarking technique with long frequency analysis window showed better transmission performance than the previous system.

Speech reinforcement based on partial masking effect

Perceived quality of the speech signal deteriorates significantly in the presence of ambient noise. In this paper, based on the analysis that the partial masking effect is a main source of the quality degradation when interfering signals are present, we propose a novel approach to enhance the perceived quality of speech signal when the ambient noise cannot be directly controlled by reinforcing it so that it can be heard more clearly. To find a suitable reinforcement rule, the loudness perception model proposed by Moore et al. [1] is adopted with the consideration on the prevention of the hearing damage. Experimental results show that the perceived quality and intelligibility can be enhanced under various noise environments.

Dual Microphone Voice Activity Detection Exploiting Interchannel Time and Level Differences

The two most important spatial cues in human auditory system may be the interaural time difference and the interaural level difference. There have been many attempts to utilize the time difference of arrival (TDoA) and level difference between two microphone signals for voice activity detection (VAD). In this letter, we propose a dual microphone VAD algorithm based on a support vector machine for which the input vector consists of both TDoA-based and level difference-based features. Several candidates for the feature combination have been compared using various TDoA-related and level difference-related features. Experimental results showed that the proposed VAD algorithm outperformed a standardized single microphone VAD, VADs based on the TDoA or level difference, and logical combination of them in various noise environments.

PARAMETRIC MULTICHANNEL NOISE REDUCTION ALGORITHM UTILIZING TEMPORAL CORRELATIONS IN REVERBERANT ENVIRONMENT

In this paper, we propose a parametric multichannel noise reduction algorithm utilizing temporal correlations in a noisy and reverberant environment. Under the reverberant condition, the received acoustic signal becomes highly correlated in the time domain and it makes successful noise reduction quite difficult. The proposed parametric noise reduction method takes account of interdependencies between components observed from different frames. Extended speech and noise power spectral density (PSD) matrices are estimated containing additional temporal information, and the parametric multichannel noise reduction filter based on these PSD matrices is applied to the input microphone array signal. According to the experimental results, the proposed algorithm has been found to show better performances compared with the conventional multiplicative filtering technique which considers the current input signals only.

A data-driven residual gain approach for two-stage speech enhancement

In this paper, we propose a novel speech enhancement algorithm based on data-driven residual gain estimation. The system consists of two stages. A noisy input signal is processed at the first stage by a conventional speech enhancement module from which both the enhanced signal and several signal-to-noise ratio (SNR)-related parameters are obtained. At the second stage, the residual gain, which is estimated by a data-driven method, is applied to the enhanced signal to further adjust it. According to the experimental results, the proposed algorithm has been found to show better performances compared with the conventional speech enhancement technique based on soft decision as well as the data-driven approach using the SNR grid look-up table.

Multichannel speech reinforcement based on binaural unmasking

Multichannel speech reinforcement exploiting DoA information is proposed.An empirical evidence of the binaural unmasking for monaural speech is provided.Proposed reinforcement restores perceived loudness considering binaural unmasking.The performance of the algorithm is verified through subjective listening tests. Speech reinforcement or near-end listening enhancement is a technique that modifies the far-end signal to mitigate the effect of the near-end noise, usually based on the power spectra of the far-end signal and the near-end noise. Psychoacoustic experiments have shown that the location of a noise source with respect to that of a signal source affects the amount of masking. Since conventional speech reinforcement methods obtain spectral gain based only on the power spectra, this psychoacoustic phenomenon called binaural unmasking has not been considered in those approaches. In this paper, we propose a novel speech reinforcement algorithm that modifies the far-end speech signal based on both the power spectrum and the direction-of-arrival (DoA) of the noise. Specifically, we have computed the equivalent frontal noise level from the observed noise level and the estimated DoA, and used it to compute spectral gains as in conventional partial loudness restoration-based speech reinforcement. Experimental results showed that the proposed method outperformed the conventional methods based on partial loudness restoration and speech intelligibility index (SII) optimization in terms of the overall perceived quality through subjective listening tests.

Decision-directed speech power spectral density matrix estimation for multichannel speech enhancement

In this letter, a multichannel decision-directed approach to estimate the speech power spectral density (PSD) matrix for multichannel speech enhancement is proposed. There have been attempts to build multichannel speech enhancement filters which depend only on the speech and noise PSD matrices, for which the accurate estimate of the clean speech PSD matrix is crucial for a successful noise reduction. In contrast to the maximum likelihood estimator which has been applied conventionally, the proposed decision-directed method is capable of tracking the time-varying speech characteristics more robustly and improves the noise reduction performance under various noise environments.

CROSSBAND FILTERING FOR STEREOPHONIC ACOUSTIC ECHO SUPPRESSION

In this paper, we propose a novel stereophonic acoustic echo suppression (SAES) technique based on crossband filtering in the short-time Fourier transform (STFT) domain. The proposed algorithm considers spectral correlations among components in adjacent frequency bins, and estimates the extended power spectral density (PSD) matrices and cross PSD vectors from the signal statistics for more precise echo estimation. In the STFT domain, the echo spectra are estimated by performing the technique without any distinguishable double-talk detector. According to the experimental results, the proposed algorithm has been found to show better performances compared with the conventional SAES method.