Singing voice detection for karaoke application

2005 ◽  
Author(s):  
Arun Shenoy ◽  
Yuansheng Wu ◽  
Ye Wang
Keyword(s):  
Singing Voice ◽  
Voice Detection

scholarly journals Research on Singing Voice Detection Based on a Long-Term Recurrent Convolutional Network with Vocal Separation and Temporal Smoothing

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1458
Author(s):  
Xulong Zhang ◽  
Yi Yu ◽  
Yongwei Gao ◽  
Xi Chen ◽  
Wei Li
Keyword(s):  
Time Domain ◽  
Short Term Memory ◽  
Block Size ◽  
Detection Algorithm ◽  
Singing Voice ◽  
Convolutional Network ◽  
Frame Size ◽  
Voice Detection ◽  
Public Datasets

Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extract essential features that reflect the audio content and characterize the vocal context in the time domain, this study adopted a long-term recurrent convolutional network (LRCN) to realize vocal detection. The convolutional layer in LRCN functions in feature extraction, and the long short-term memory (LSTM) layer can learn the time sequence relationship. The preprocessing of singing voices and accompaniment separation and the postprocessing of time-domain smoothing were combined to form a complete system. Experiments on five public datasets investigated the impacts of the different features for the fusion, frame size, and block size on LRCN temporal relationship learning, and the effects of preprocessing and postprocessing on performance, and the results confirm that the proposed singing voice detection algorithm reached the state-of-the-art level on public datasets.

scholarly journals Singing Voice Detection in Opera Recordings: A Case Study on Robustness and Generalization

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1214
Author(s):  
Michael Krause ◽  
Meinard Müller ◽  
Christof Weiß
Keyword(s):  
Machine Learning ◽  
Traditional Approach ◽  
Training Dataset ◽  
Singing Voice ◽  
Audio Recordings ◽  
Voice Detection ◽  
Music Information ◽  
Music Audio ◽  
Recorded Performances

Automatically detecting the presence of singing in music audio recordings is a central task within music information retrieval. While modern machine-learning systems produce high-quality results on this task, the reported experiments are usually limited to popular music and the trained systems often overfit to confounding factors. In this paper, we aim to gain a deeper understanding of such machine-learning methods and investigate their robustness in a challenging opera scenario. To this end, we compare two state-of-the-art methods for singing voice detection based on supervised learning: A traditional approach relying on hand-crafted features with a random forest classifier, as well as a deep-learning approach relying on convolutional neural networks. To evaluate these algorithms, we make use of a cross-version dataset comprising 16 recorded performances (versions) of Richard Wagner’s four-opera cycle Der Ring des Nibelungen. This scenario allows us to systematically investigate generalization to unseen versions, musical works, or both. In particular, we study the trained systems’ robustness depending on the acoustic and musical variety, as well as the overall size of the training dataset. Our experiments show that both systems can robustly detect singing voice in opera recordings even when trained on relatively small datasets with little variety.

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