ABSTRACT
We propose an in-ear sensing method that automatically detects vocal reactions that people often exhibit when listening to music. We observe what kind of vocal reactions are often brought during music listening and investigate the challenges of applying an existing representative acoustic classification model to vocal reaction recognition. We present our vocal reaction recognition method and the preliminary evaluation to assess its performance.
- [n.d.]. Music Listening 2019. https://www.ifpi.org/wp-content/uploads/2020/07/Music-Listening-2019-1.pdf. Accessed: September 30, 2020.Google Scholar
- [n.d.]. YAMNet. https://github.com/tensorflow/models/tree/master/research/audioset/yamnet. Accessed: September 23, 2020.Google Scholar
- Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017).Google Scholar
Index Terms
Automatic recognition of vocal reactions in music listening using smart earbuds: poster abstract
Recommendations
Impact of vocal effort variability on automatic speech recognition
The impact of changes in a speaker's vocal effort on the performance of automatic speech recognition has largely been overlooked by researchers and virtually no speech resources exist for the development and testing of speech recognizers at all vocal ...
Word level automatic alignment of music and lyrics using vocal synthesis
We propose a signal-based approach instead of the commonly used model-based approach, to automatically align vocal music with text lyrics at the word level. In this approach, we use a text-to-speech system to synthesize the singing voice according to ...





Comments