Browsing by Author "Huang, Mao-Chang"

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A Two-stage Singing Voice Separation Algorithm Using Spectro-temporal Modulation Features
(2015)
Yen, Frederick Z.
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Huang, Mao-Chang
;
Chi, Tai-Shih
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電機學院
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College of Electrical and Computer Engineering
A two-stage singing voice separation algorithm using spectrotemporal modulation features is proposed in this paper. First, music clips are transformed into auditory spectrograms and the spectral-temporal modulation contents of all time-frequency (T-F) units of the auditory spectrograms are extracted using an auditory model. Then, T-F units are sequentially clustered using the expectation-maximization (EM) algorithm into percussive, harmonic and vocal units through the proposed two-stage algorithm. Lastly, the singing voice is synthesized from clustered vocal T-F units via time-frequency masking. The algorithm was evaluated using the MIR-1K dataset and demonstrated better separation results than our previously proposed one-stage algorithm.
Acoustic Scene Change Detection by Spectro-Temporal Filtering on Spectrogram using Chirps
(2016)
Lin, Chang-Hong
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Cheong, Kah-Meng
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Huang, Mao-Chang
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Chen, Ming-Yen
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Chang, Chen-Kuei
Indoor acoustic scene change detection systems using periodic impulse signals were developed in the past. Comparing with impulse signals, the chirp signal is more robust against environmental noise due to its specific spectro-temporal structure, which can be easily detected using a spectro-temporal modulation filtering mechanism. In this paper, we demonstrate a system which employs the two-dimensional spectro-temporal filtering mechanism on Fourier spectrogram to measure the total energy of reverberations of the chirp signal and compares the energy difference between consecutive chirps with a predefined threshold to automatically detect the change of the acoustic scene. Simulation results show the proposed system is very effective in detecting the acoustic scene change in a real living room with great robustness against noise recorded from real-world.
Interaural Coherence Induced Ideal Binary Mask for Binaural Speech Separation and Dereverberation
(2016-01-01)
Chen, Yi-Ting
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Chen, Tzu-Hao
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Huang, Mao-Chang
;
Chi, Tai-Shih
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電機工程學系
We have proposed a spatial-cue based binaural noise reduction algorithm for hearing aids. However, in that algorithm, decision parameters are empirically selected. In this paper, we extend the work and propose a supervised classification algorithm for binaural speech enhancement/separation and dereverberation using a modified ideal binary mask (mIBM) as the training target and simple neural networks (NNs) as classifiers. The low complexity of the simple NNs makes the proposed algorithm practical for binaural hearing aids. The interaural time difference (ITD) and the interaural level difference (ILD) of each T-F unit are extracted as the basic binaural features. For the purpose of dereverberation, the interaural coherence (IC) is also considered when building the target mIBM and training the NNs. For separation evaluations, our method yields comparable performance to a more complicated benchmark system, which cannot de-reverb the signal. For concurrent separation and dereverberation, our method offers 4 to 5 dB improvement on the frequencyweighted segmental speech-to-noise ratio (SNRfw) over unprocessed speech.