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PhiNet: Speaker Verification with Phonetic Interpretability
Yi Ma, Shuai Wang, Tianchi Liu +1
Despite remarkable progress, automatic speaker verification (ASV) systems typically lack the transparency required for high-accountability applications. Motivated by how human expe…
Should Audio Front-ends be Adaptive? Comparing Learnable and Adaptive Front-ends
Qiquan Zhang, Buddhi Wickramasinghe, Eliathamby Ambikairajah +2
Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications. Recently, there has been a growing interest in learnable…
Self-Transriber: Few-shot Lyrics Transcription with Self-training
Xiaoxue Gao, Xianghu Yue, Haizhou Li
The current lyrics transcription approaches heavily rely on supervised learning with labeled data, but such data are scarce and manual labeling of singing is expensive. How to bene…
PoLyScriber: Integrated Fine-tuning of Extractor and Lyrics Transcriber for Polyphonic Music
Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility. Typically, lyrics transcription can be performed by a two-step pipel…
Music-robust Automatic Lyrics Transcription of Polyphonic Music
Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
Lyrics transcription of polyphonic music is challenging because singing vocals are corrupted by the background music. To improve the robustness of lyrics transcription to the backg…