Browsing by Author "Hsu, Chung-Chien"

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A HEARING MODEL TO ESTIMATE MANDARIN SPEECH INTELLIGIBILITY FOR THE HEARING IMPAIRED PATIENTS
(2015)
Tsai, Pei-Chun
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Lin, Shih-Ting
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Lee, Wen-Chung
;
Hsu, Chung-Chien
;
Chi, Tai-Shih
A hearing model, which is parameterized by hearing thresholds, degrees of loudness recruitment and reductions of frequency resolution of a hearing-impaired (HI) patient, is proposed in this paper. The model is developed in the filter-bank framework and is flexible for fitting hearing-loss conditions of HI patients. Psychoacoustic experiments were conducted under clean and noisy conditions to validate the model\'s capability in predicting Mandarin speech intelligibility for HI patients. Statistical analysis on the hearing-test results suggests that the proposed model can predict Mandarin speech intelligibility for HI patients to a certain degree.
A HEARING MODEL TO ESTIMATE MANDARIN SPEECH INTELLIGIBILITY FOR THE HEARING IMPAIRED PATIENTS
(2015-01-01)
Tsai, Pei-Chun
;
Lin, Shih-Ting
;
Lee, Wen-Chung
;
Hsu, Chung-Chien
;
Chi, Tai-Shih
A hearing model, which is parameterized by hearing thresholds, degrees of loudness recruitment and reductions of frequency resolution of a hearing-impaired (HI) patient, is proposed in this paper. The model is developed in the filter-bank framework and is flexible for fitting hearing-loss conditions of HI patients. Psychoacoustic experiments were conducted under clean and noisy conditions to validate the model's capability in predicting Mandarin speech intelligibility for HI patients. Statistical analysis on the hearing-test results suggests that the proposed model can predict Mandarin speech intelligibility for HI patients to a certain degree.
A Non-uniformly Distributed Three-microphone Array for Speech Enhancement in Directional and Diffuse Noise Field
(2014-01-01)
Hsu, Chung-Chien
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Cheong, Kah-Meng
;
Chi, Tai-Shih
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
This paper proposes a non-uniformly distributed three-microphone array speech enhancement system to suppress directional interferences and diffuse noise simultaneously. Each pair of microphones is designed to tackle one kind of noise. Unlike other hybrid systems, which combine different noise suppression techniques derived in different domains, the proposed system integrates two noise suppression techniques derived in the unified power spectral density domain. In terms of objective LLR and PESQ measures, the proposed hybrid system is demonstrated more effective in eliminating directional and diffuse noise under various SNR conditions than each individual suppression technique.
Bayesian Factorization and Selection for Speech and Music Separation
(2014-01-01)
Yang, Po-Kai
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Hsu, Chung-Chien
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Chien, Jen-Tzung
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電機工程學系
;
Department of Electrical and Computer Engineering
This paper proposes a new Bayesian nonnegative matrix factorization (NMF) for speech and music separation. We introduce the Poisson likelihood for NMF approximation and the exponential prior distributions for the factorized basis matrix and weight matrix. A variational Bayesian (VB) EM algorithm is developed to implement an efficient solution to variational parameters and model parameters for Bayesian NMF. Importantly, the exponential prior parameter is used to control the sparseness in basis representation. The variational lower bound in VB-EM procedure is derived as an objective to conduct adaptive basis selection for different mixed signals. The experiments on single-channel speech/music separation show that the adaptive basis representation in Bayesian NMF via model selection performs better than the NMF with the fixed number of bases in terms of signal-to-distortion ratio.
Binary Mask Estimation Based on Frequency Modulations
(2014-01-01)
Hsu, Chung-Chien
;
Chien, Jen-Tzung
;
Chi, Tai-Shih
;
電機工程學系
;
Department of Electrical and Computer Engineering
In this paper, a binary mask estimation algorithm is proposed based on modulations of speech. A multi-resolution spectro-temporal analytical auditory model is utilized to extract modulation features to estimate the binary mask, which is often used in speech segregation applications. The proposed method estimates noise from the beginning of each test sentence, a common approach seen in many conventional speech enhancement algorithms, to further enhance the modulation features. Experimental results demonstrate that the proposed method outperforms the AMS-GMM system in terms of the HIT-FA rate when estimating the binary mask.
DISCRIMINATIVE DEEP RECURRENT NEURAL NETWORKS FOR MONAURAL SPEECH SEPARATION
(2016)
Wang, Guan-Xiang
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Hsu, Chung-Chien
;
Chien, Jen-Tzung
;
電機工程學系
;
Department of Electrical and Computer Engineering
Deep neural network is now a new trend towards solving different problems in speech processing. In this paper, we propose a discriminative deep recurrent neural network (DRNN) model for monaural speech separation. Our idea is to construct DRNN as a regression model to discover the deep structure and regularity for signal reconstruction from a mixture of two source spectra. To reinforce the discrimination capability between two separated spectra, we estimate DRNN separation parameters by minimizing an integrated objective function which consists of two measurements. One is the within source reconstruction errors due to the individual source spectra while the other conveys the discrimination information which preserves the mutual difference between two source spectra during the supervised training procedure. This discrimination information acts as a kind of regularization so as to maintain between-source separation in monaural source separation. In the experiments, we demonstrate the effectiveness of the proposed method for speech separation compared with the other methods.
Discriminative Layered Nonnegative Matrix Factorization for Speech Separation
(2016-01-01)
Hsu, Chung-Chien
;
Chi, Tai-Shih
;
Chien, Jen-Tzung
;
電機工程學系
;
Department of Electrical and Computer Engineering
This paper proposes a discriminative layered nonnegative matrix factorization (DL-NMF) for monaural speech separation. The standard NMF conducts the parts-based representation using a single-layer of bases which was recently upgraded to the layered NMF (L-NMF) where a tree of bases was estimated for multi-level or multi-aspect decomposition of a complex mixed signal. In this study, we develop the DL-NMF by extending the generative bases in L-NMF to the discriminative bases which are estimated according to a discriminative criterion. The discriminative criterion is conducted by optimizing the recovery of the mixed spectra from the separated spectra and minimizing the reconstruction errors between separated spectra and original source spectra. The experiments on single-channel speech separation show the superiority of DL-NMF to NMF and L-NMF in terms of the SDR, SIR and SAR measures.
FFT-BASED SPECTRO-TEMPORAL ANALYSIS AND SYSTNESIS OF SOUNDS
(2011)
Hsu, Chung-Chien
;
Lin, Ting-Han
;
Chi, Tai-Shih
;
電機工程學系
;
Department of Electrical and Computer Engineering
The concept of the two-dimensional spectro-temporal modulation filtering of the auditory model [1] is implemented for the FFT spectrogram. It analyzes the spectrogram in terms of the temporal dynamics and the spectral structures of the sound. The overlap and add (OLA) method, which is more convenient and reliable than the iterative-projection method proposed in [1], is used to invert the FFT spectrogram back to sounds. The Non-Negative Sparse Coding (NNSC) method is adopted to demonstrate the benefit of our analysis-synthesis procedures in a noise suppression application. Even without fine-tuning parameters, our proposed analysis-synthesis procedures offer benefits in de-noising especially under low SNR conditions.
Layered Nonnegative Matrix Factorization for Speech Separation
(2015)
Hsu, Chung-Chien
;
Chien, Jen-Tzung
;
Chi, Tai-Shih
;
電機學院
;
College of Electrical and Computer Engineering
This paper proposes a layered nonnegative matrix factorization (L-NMF) algorithm for speech separation. The standard NMF method extracts parts-based bases out of nonnegative training data and is often used to separate mixed spectrograms. The proposed L-NMF algorithm comprises of several layers of standard NMF blocks. During training, each layer of the L-NMF is initialized separately and then fine-tuned by minimizing the propagated reconstruction error. More complicated bases of the training data are emerged in deeper layers of the L-NMF by progressively combining parts-based bases extracted in the first layer. In other words, these complicated bases contain collective information of the parts-based bases. The bases deciphered by all layers are then used to separate spectrograms in the conventional NMF way. Simulation results show the proposed L-NMF outperforms the standard NMF in terms of the source-to-distortion ratio (SDR).
MODULATION WIENER FILTER FOR IMPROVING SPEECH INTELLIGIBILITY
(2015-01-01)
Hsu, Chung-Chien
;
Cheong, Kah-Meng
;
Chien, Jen-Tzung
;
Chi, Tai-Shih
;
電機資訊學士班
This paper presents a single-channel high-dimensional Wiener filter in the spectro-temporal modulation domain. Unlike other conventional noise reduction techniques, the proposed algorithm not only reduces noise but also enhances the "textures" of the speech signal. A non-iterative decision-directed noise estimation method is adopted to estimate the modulation SNR for the modulation-domain Wiener filter. The efficacy of the proposed algorithm in enhancing speech intelligibility is assessed using the short-time objective intelligibility (STOI) measure. Statistical analysis results demonstrate that our proposed algorithm can improve STOI scores in speech-shape noise (SSN) and white noise conditions, but not in babble noise condition, while the conventional Wiener filter fails to improve STOI scores in all three noise conditions.
MODULATION WIENER FILTER FOR IMPROVING SPEECH INTELLIGIBILITY
(2015-01-01)
Hsu, Chung-Chien
;
Cheong, Kah-Meng
;
Chien, Jen-Tzung
;
Chi, Tai-Shih
;
電機工程學系
This paper presents a single-channel high-dimensional Wiener filter in the spectro-temporal modulation domain. Unlike other conventional noise reduction techniques, the proposed algorithm not only reduces noise but also enhances the "textures" of the speech signal. A non-iterative decision-directed noise estimation method is adopted to estimate the modulation SNR for the modulation-domain Wiener filter. The efficacy of the proposed algorithm in enhancing speech intelligibility is assessed using the short-time objective intelligibility (STOI) measure. Statistical analysis results demonstrate that our proposed algorithm can improve STOI scores in speech-shape noise (SSN) and white noise conditions, but not in babble noise condition, while the conventional Wiener filter fails to improve STOI scores in all three noise conditions.
Multiband analysis and synthesis of spectro-temporal modulations of Fourier spectrogram
(2011-05-01)
Chi, Tai-Shih
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Hsu, Chung-Chien
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電機工程學系
;
Department of Electrical and Computer Engineering
The two-dimensional spectro-temporal modulation filtering concept of the auditory model [T. Chi, P. Ru, and S. A. Shamma, J. Acoust. Soc. Am. 118(2), 887-906 (2005)] is implemented on the Fourier spectrogram. The Fourier magnitude spectrogram is analyzed in terms of its joint spectro-temporal modulations, which embed the temporal dynamics and spectral structures. Instead of iterative projection methods, the overlap-and-add method is adopted to invert modified Fourier spectrograms back to sounds. The proposed framework not only provides a similar spectro-temporal analytical process for sounds as the auditory model but also produces synthesized sounds with better quality in a timely manner, which makes proposed framework feasible to human speech recognition (HSR) applications as well. (C) 2011 Acoustical Society of America
Robust Voice Activity Detection Algorithm Based on Feature of Frequency Modulation of Harmonics and Its DSP Implementation
(2015-10-01)
Hsu, Chung-Chien
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Cheong, Kah-Meng
;
Chi, Tai-Shih
;
Tsao, Yu
;
電機工程學系
This paper proposes a voice activity detection (VAD) algorithm based on an energy related feature of the frequency modulation of harmonics. A multi-resolution spectro-temporal analysis framework, which was developed to extract texture features of the audio signal from its Fourier spectrogram, is used to extract frequency modulation features of the speech signal. The proposed algorithm labels the voice active segments of the speech signal by comparing the energy related feature of the frequency modulation of harmonics with a threshold. Then, the proposed VAD is implemented on one of Texas Instruments (TI) digital signal processor (DSP) platforms for real-time operation. Simulations conducted on the DSP platform demonstrate the proposed VAD performs significantly better than three standard VADs, ITU-T G.729B, ETSI AMR1 and AMR2, in non-stationary noise in terms of the receiver operating characteristic (ROC) curves and the recognition rates from a practical distributed speech recognition (DSR) system.
Spectral Modulation Sensitivity Based Perceptual Acoustic Echo Cancellation
(2013-01-01)
Chuang, Wei-Lun
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Cheong, Kah-Meng
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Hsu, Chung-Chien
;
Chi, Tai-Shih
;
電機工程學系
Perceptual acoustic echo cancellers were developed by mainly considering human hearing thresholds of different acoustic frequencies in the past. In addition to the different frequency sensitivities, the human brain further analyzes sounds in terms of their spectral and temporal modulations. In this paper, we extend the perceptual normalized least mean square (P-NLMS) algorithm by adding a second pre-emphasis filter which accounts for brain's spectral modulation sensitivity. Simulation results and listening tests demonstrate our system effectively reduces the perceived residual echo during convergence due to faster convergence rates in hearing-sensitive spectral modulation bands and acoustic frequency bands.
Spectro-temporal Modulation Based Singing Detection Combined with Pitch based Grouping for Singing Voice Separation
(2013-01-01)
Lin, Tse-En
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Hsu, Chung-Chien
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Chen, Yi-Cheng
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Chen, Jian-Hueng
;
Chi, Tai-Shih
A spectro-temporal modulation based singing voice detection cascaded with a Viterbi based pitch tracking algorithm is proposed in this paper for singing-voice separation from monaural recordings. To detect the singing voice, the spectrotemporal modulation energy related to voice harmonics is extracted using a spectro-temporal modulation analysis framework developed for the Fourier spectrogram. Separation of singing -voice from background music is conducted using a binary mask to group estimated harmonics of singing voice. The proposed system is evaluated using MIR-1K dataset and is shown outperforming three other binary-mask based systems in the vocal/music separation task.
Spectro-temporal modulation energy based mask for robust speaker identification
(2012-05-01)
Chi, Tai-Shih
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Lin, Ting-Han
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Hsu, Chung-Chien
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電機工程學系
;
Department of Electrical and Computer Engineering
Spectro-temporal modulations of speech encode speech structures and speaker characteristics. An algorithm which distinguishes speech from non-speech based on spectro-temporal modulation energies is proposed and evaluated in robust text-independent closed-set speaker identification simulations using the TIMIT and GRID corpora. Simulation results show the proposed method produces much higher speaker identification rates in all signal-to-noise ratio (SNR) conditions than the baseline system using mel-frequency cepstral coefficients. In addition, the proposed method also outperforms the system, which uses auditory-based nonnegative tensor cepstral coefficients [Q. Wu and L. Zhang, "Auditory sparse representation for robust speaker recognition based on tensor structure," EURASIP J. Audio, Speech, Music Process. 2008, 578612 (2008)], in low SNR (<= 10 dB) conditions. (C) 2012 Acoustical Society of America
SPECTRO-TEMPORAL SUBBAND WIENER FILTER FOR SPEECH ENHANCEMENT
(2012)
Hsu, Chung-Chien
;
Lin, Tse-En
;
Chen, Jian-Hueng
;
Chi, Tai-Shih
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電子工程學系及電子研究所
In this paper, we propose a signal-channel speech enhancement algorithm by applying the conventional Wiener filter in the spectro-temporal modulation domain. The multi-resolution spectro-temporal analysis and synthesis framework for Fourier spectrograms [12] is extended to the analysis-modification-synthesis (AMS) framework for speech enhancement. Compared with conventional speech enhancement algorithms, a Wiener filter and an extended minimum mean-square error (MMSE) algorithm, our proposed method outperforms them by a large/small margin in white/babble noise conditions from both objective and subjective evaluations.
VOICE ACTIVITY DETECTION BASED ON FREQUENCY MODULATION OF HARMONICS
(2013)
Hsu, Chung-Chien
;
Lin, Tse-En
;
Chen, Jian-Hueng
;
Chi, Tai-Shih
;
電機資訊學士班
In this paper, we propose a voice activity detection (VAD) algorithm based on spectro-temporal modulation structures of input sounds. A multi-resolution spectro-temporal analysis framework is used to inspect prominent speech structures. By comparing with an adaptive threshold, the proposed VAD distinguishes speech from non-speech based on the energy of the frequency modulation of harmonics. Compared with three standard VADs, ITU-T G. 729B, ETSI AMR1 and AMR2, our proposed VAD significantly outperforms them in non-stationary noises in terms of the receiver operating characteristic (ROC) curves and the recognition rates from a practical distributed speech recognition (DSR) system.