Monaural Music Source Separation Using Deep Convolutional Neural Network Embedded with Feature Extraction Module

Yu, Yongbin; Peng, Chenhui; Tang, Qian; Wang, Xiangxiang

doi:10.1109/cacml55074.2022.00098

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…CNN has been applied to research in various fields with different case studies of voice detections. This research includes voice detection to classify voices [22], [23], detect depressive or emotional voices [24], [25], detect music [26], realize voice-based security systems [27], [28], detect language phonemes [29], [30], detect disease by sounds [31]- [34], and identify animal voices [35], [36]. There are many other research case studies that can be found and used as ongoing future research.…”

Section: A Transfer Learning Modelmentioning

confidence: 99%

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Edwin Ariesto Umbu Malahina

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Studi penelitian ini dilaksanakan untuk mengembangkan sistem deteksi fonetik dialek Kambera dari bahasa lokal Sumba Timur berbasis framework TensorFlow yang akan diimplementasikan pada aplikasi mobile. Sebagai bagian dari inisiatif ini, penelitian ini telah mengompilasikan sebuah dataset representatif dari sampel fonetik dialek Kambera. Tujuan utamanya adalah untuk meningkatkan presisi dalam pengenalan fonetik. Menggunakan dialek Kambera sebagai studi kasus, data diekstraksi dan dilatih menggunakan layanan Teachable Machine berbasis open-source. Penelitian ini mengadopsi pendekatan berbasis convolutional neural network (CNN) yang dikombinasikan dengan metode Mel frequency cepstral coefficients (MFCC) untuk ekstraksi fitur yang lebih akurat. Setelah proses pengumpulan data, pelatihan model, pengujian, dan implementasi, model diintegrasikan ke dalam platform Android untuk kepentingan publik yang ingin memahami bahasa dialek Kambera dari Sumba Timur. Pengembangan dan pengujian sistem ini dirancang untuk mendeteksi dan menginterpretasikan fonetik bahasa lokal Sumba Timur dengan dialek Kambera, memberikan kontribusi signifikan dalam optimalisasi pengenalan fonetik, serta menyediakan dataset untuk kepentingan penelitian berkelanjutan. Sistem ini juga berfungsi sebagai alat edukasi linguistik yang dapat diakses dan mendukung inklusi serta diversifikasi linguistik dalam teknologi digital. Evaluasi empiris menunjukkan bahwa tingkat presisi deteksi dialek rata-rata secara keseluruhan mencapai 98,3% hingga 99,6%, dengan tingkat kepuasan pengujian kepada pengguna mencapai 99,33%. Hasil ini menegaskan bahwa sistem yang dikembangkan memiliki kapabilitas deteksi yang sangat efisien dan baik.

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Section: A Transfer Learning Modelmentioning

confidence: 99%

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Edwin Ariesto Umbu Malahina

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…Until recently, various research strategies and algorithms have been introduced to improve the separation results in SVS tasks [ 22 , 23 ]. The deep learning techniques [ 24 , 25 , 26 , 27 ] are perhaps the most widely used for SVS. Yu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Yu et al. [ 24 ] proposed a new feature-extraction module based on UNet++ for SVS. An enhanced encoder–decoder is first created to initially extract multiscale information from a magnitude spectrogram of the mixed music.…”

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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Monaural Music Source Separation Using Deep Convolutional Neural Network Embedded with Feature Extraction Module

Cited by 2 publications

References 19 publications

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

Teachable Machine: Deteksi Dialek Sumba Timur (Kambera) Menggunakan Layanan Open Source

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

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