Model Order Selection for Wind Noise Reduction in Non-negative Matrix Factorization
In this paper, we propose a wind noise reduction method based on various types of non-negative matrix factorization (NMF) approaches. Since wind noise has highly non- stationary spectral characteristics that are difficult to remove using stochastic oriented methods, it is more effective to use template matching-based methods.
We first investigate whether the quality of enhanced output varies depending on the ratio of the size of speech models to that of noise models. We especially show that the optimal ratio is related to the signal-to-noise ratio (SNR) of the input signal. Based on the results of analysis, we propose an efficient algorithm for adaptively changing the size of speech and noise NMF models in each analysis frame. Since the proposed algorithm takes into account the trade- off relationship between speech distortion and noise reduction, its output quality becomes very natural. The experimental results also confirm the superiority of the proposed algorithm to conventional template matching based algorithms.
|294||International Conference||Seyun Um, Sangshin Oh, Kyungguen Byun, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "Emotional Speech Synthesis with Rich and Granularized Control" in ICASSP, 2020|
|293||International Conference||Min-Jae Hwang, Eunwoo Song, Ryuichi Yamamoto, Frank Soong, Hong-Goo Kang "Improving LPCNet-based Text-to-Speech with Linear Prediction-structured Mixture Density Network" in ICASSP, 2020|
|292||International Journal||Soo-Whan Chung, Joon Son Chung, Hong Goo Kang "Perfect Match: Self-Supervised Embeddings for Cross-Modal Retrieval" in IEEE Journal of Selected Topics in Signal Processing, vol.14, issue 3, 2020|
|291||International Conference||Hyeonjoo Kang, Young-Sun Joo, Inseon Jang, Chunghyun Ahn, Hong-Goo Kang "A Study on Acoustic Parameter Selection Strategies to Improve Deep Learning-Based Speech Synthesis" in APSIPA, 2019|
|290||International Journal||Ohsung Kwon, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "An Effective Style Token Weight Control Technique for End-to-End Emotional Speech Synthesis" in IEEE Signal Processing Letters, vol.26, issue 9, pp.1383-1387, 2019|
|289||International Conference||Min-Jae Hwang, Hong-Goo Kang "Parameter enhancement for MELP speech codec in noisy communication environment" in INTERSPEECH, 2019|
|288||Domestic Journal||오상신, 엄세연, 장인선, 안충현, 강홍구 "k-평균 알고리즘을 활용한 음성의 대표 감정 스타일 결정 방법" in 한국음향학회지, vol.38, 제 5호, pp.614-620, 2019|
|287||International Journal||Jinkyu Lee, Hong-Goo Kang "A Joint Learning Algorithm for Complex-Valued T-F Masks in Deep Learning-Based Single-Channel Speech Enhancement Systems" in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.27, issue 6, pp.1098-1108, 2019|
|286||International Conference||Keulbit Kim, Jinkyu Lee, Jan Skoglund, Hong-Goo Kang "Model Order Selection for Wind Noise Reduction in Non-negative Matrix Factorization" in ITC-CSCC, 2019|
|285||International Conference||Ohsung Kwon, Inseon Jang, ChungHyun Ahn, Hong-Goo Kang "Emotional Speech Synthesis Based on Style Embedded Tacotron2 Framework" in ITC-CSCC, 2019|