Browsing by Author "Chen, Ming-Tso"

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A HYBRID NEURAL NETWORK BASED ON THE DUPLEX MODEL OF PITCH PERCEPTION FOR SINGING MELODY EXTRACTION
(2018-01-01)
Chou, Hsin
;
Chen, Ming-Tso
;
Chi, Tai-Shih
;
電機工程學系
;
Department of Electrical and Computer Engineering
In this paper, we build up a hybrid neural network (NN) for singing melody extraction from polyphonic music by imitating human pitch perception. For human hearing, there are two pitch perception models, the spectral model and the temporal model, in accordance with whether harmonics are resolved or not. Here, we first use NNs to implement individual models and evaluate their performance in the task of singing melody extraction. Then, we combine the NNs to constitute the composite NN to simulate the duplex model, which complements the pitch perception from unresolved harmonics of the spectral model using the temporal model. Simulation results show the proposed composite NN outperforms other conventional methods in singing melody extraction.
CNN BASED TWO-STAGE MULTI-RESOLUTION END-TO-END MODEL FOR SINGING MELODY EXTRACTION
(2019-01-01)
Chen, Ming-Tso
;
Li, Bo-Jun
;
Chi, Tai-Shih
;
電機工程學系
;
Department of Electrical and Computer Engineering
Inspired by human hearing perception, we propose a two-stage multi-resolution end-to-end model for singing melody extraction in this paper. The convolutional neural network (CNN) is the core of the proposed model to generate multi-resolution representations. The 1-D and 2-D multi-resolution analysis on waveform and spectrogram-like graph are successively carried out by using 1-D and 2-D CNN kernels of different lengths and sizes. The 1-D CNNs with kernels of different lengths produce multi-resolution spectrogram-like graphs without suffering from the trade-off between spectral and temporal resolutions. The 2-D CNNs with kernels of different sizes extract features from spectro-temporal envelopes of different scales. Experiment results show the proposed model outperforms three compared systems in three out of five public databases.
SINGING VOICE CORRECTION USING CANONICAL TIME WARPING
(2018-01-01)
Luo, Yin-Jyun
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Chen, Ming-Tso
;
Chi, Tai-Shih
;
Su, Li
;
電機工程學系
Expressive singing voice correction is an appealing but challenging problem. A robust time-warping algorithm which synchronizes two singing recordings can provide a promising solution. We thereby propose to address the problem by canonical time warping (CTW) which aligns amateur singing recordings to professional ones. A new pitch contour is generated given the alignment information, and a pitch-corrected singing is synthesized back through the vocoder. The objective evaluation shows that CTW is robust against pitch-shifting and time-stretching effects, and the subjective test demonstrates that CTW prevails the other methods including DTW and the commercial auto-tuning software. Finally, we demonstrate the applicability of the proposed method in a practical, real-world scenario.