Development of accurate automated language identification model using polymer pattern and tent maximum absolute pooling techniques

Tuncer, Türker; Doğan, Şengül; Akbal, Erhan; Cicekli, Abdullah; Acharya, U. Rajendra

doi:10.1007/s00521-021-06678-0

Cited by 7 publications

(1 citation statement)

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“…Singing voice separation (SVS) has drawn a lot of interest and consideration in many downstream applications [ 1 , 2 , 3 , 4 ]. It deals with the technique of separating a singing voice or background from a mix of music, which is a crucial strategy for singer identification [ 5 , 6 ], music information retrieval [ 7 , 8 ], lyric recognition and alignment [ 9 , 10 , 11 , 12 ], song language identification [ 13 , 14 ], and chord recognition [ 15 , 16 , 17 ]. The recent separation techniques, however, fall well short of the capabilities of human hearing.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Single-Channel Singing Voice Separation with Weighted Robust Principal Component Analysis Based on Gammatone Auditory Filterbank and Vocal Activity Detection

Wang

2023

Sensors

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Singing-voice separation is a separation task that involves a singing voice and musical accompaniment. In this paper, we propose a novel, unsupervised methodology for extracting a singing voice from the background in a musical mixture. This method is a modification of robust principal component analysis (RPCA) that separates a singing voice by using weighting based on gammatone filterbank and vocal activity detection. Although RPCA is a helpful method for separating voices from the music mixture, it fails when one single value, such as drums, is much larger than others (e.g., the accompanying instruments). As a result, the proposed approach takes advantage of varying values between low-rank (background) and sparse matrices (singing voice). Additionally, we propose an expanded RPCA on the cochleagram by utilizing coalescent masking on the gammatone. Finally, we utilize vocal activity detection to enhance the separation outcomes by eliminating the lingering music signal. Evaluation results reveal that the proposed approach provides superior separation outcomes than RPCA on ccMixter and DSD100 datasets.

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