Recognizing emotions in the singing voice.

2017 ◽  
Vol 27 (4) ◽  
pp. 244-255 ◽  
Author(s):  
Klaus R. Scherer ◽  
Stéphanie Trznadel ◽  
Bernardino Fantini ◽  
Johan Sundberg
Keyword(s):  
Singing Voice

Korean Singing Voice Synthesis Based on an LSTM Recurrent Neural Network

2018 ◽  
Author(s):  
Juntae Kim ◽  
Heejin Choi ◽  
Jinuk Park ◽  
Minsoo Hahn ◽  
Sangjin Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Singing Voice ◽  
Voice Synthesis

Opera and the South African Political

2019 ◽  
pp. 289-311
Author(s):  
Christopher Ballantine
Keyword(s):  
South Africa ◽  
South African ◽  
Singing Voice ◽  
The South ◽  
Sociopolitical Context ◽  
Creating Meaning ◽  
Empirical Means

Christopher Ballantine’s focus is on timbre, in particular the timbre of the singing voice, and how this combines with the imagination to create meaning. His investigation is largely philosophical; but the growth in popularity of opera in post-apartheid South Africa provides empirical means for Ballantine to indicate this powerful but analytically neglected way of creating meaning in the performance of music. His case study shows how timbre can produce musical experiences that have a particular, and often surprising, resonance. Through interviews with leading figures in South African opera, Ballantine demonstrates that timbre is a vital wellspring of imagined meaning; it should especially be seen thus if we seek to understand the singing voice in a sociopolitical context such as that of South Africa during and after apartheid.

Music in the British Workplace: From Voice to a Technique of Control?

2020 ◽  
Author(s):  
Marek Korczynski
Keyword(s):  
Twentieth Century ◽  
Social Control ◽  
Social History ◽  
Singing Voice ◽  
Industrial Noise ◽  
The Third ◽  
The Social ◽  
History Of

This chapter examines music in the British workplace. It considers whether it is appropriate to see the history of music in the workplace as involving a journey from the organic singing voice (both literal and metaphorical) of workers to broadcast music appropriated by the powerful to become a technique of social control. The chapter charts four key stages in the social history of music in British workplaces. First, it highlights the existence of widespread cultures of singing at work prior to industrialization, and outlines the important meanings these cultures had for workers. Next, it outlines the silencing of the singing voice within the workplace further to industrialization—either from direct employer bans on singing, or from the roar of the industrial noise. The third key stage involves the carefully controlled employer- and state-led reintroduction of music in the workplace in the mid-twentieth century—through the centralized relaying of specific forms of music via broadcast systems in workplaces. The chapter ends with an examination of contemporary musicking in relation to (often worker-led) radio music played in workplaces.

scholarly journals Audio-to-score singing transcription based on a CRNN-HSMM hybrid model

2021 ◽  
Vol 10 ◽  
Author(s):  
Ryo Nishikimi ◽  
Eita Nakamura ◽  
Masataka Goto ◽  
Kazuyoshi Yoshii
Keyword(s):  
Hybrid Model ◽  
Viterbi Algorithm ◽  
Language Model ◽  
Expressive Power ◽  
Acoustic Model ◽  
Musical Language ◽  
Musical Score ◽  
Singing Voice ◽  
Generative Process ◽  
Music Signal

This paper describes an automatic singing transcription (AST) method that estimates a human-readable musical score of a sung melody from an input music signal. Because of the considerable pitch and temporal variation of a singing voice, a naive cascading approach that estimates an F0 contour and quantizes it with estimated tatum times cannot avoid many pitch and rhythm errors. To solve this problem, we formulate a unified generative model of a music signal that consists of a semi-Markov language model representing the generative process of latent musical notes conditioned on musical keys and an acoustic model based on a convolutional recurrent neural network (CRNN) representing the generative process of an observed music signal from the notes. The resulting CRNN-HSMM hybrid model enables us to estimate the most-likely musical notes from a music signal with the Viterbi algorithm, while leveraging both the grammatical knowledge about musical notes and the expressive power of the CRNN. The experimental results showed that the proposed method outperformed the conventional state-of-the-art method and the integration of the musical language model with the acoustic model has a positive effect on the AST performance.

scholarly journals Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System

2021 ◽  
pp. 1-1
Author(s):  
Yukiya Hono ◽  
Kei Hashimoto ◽  
Keiichiro Oura ◽  
Yoshihiko Nankaku ◽  
Keiichi Tokuda
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Singing Voice ◽  
Synthesis System ◽  
Voice Synthesis
Sign in / Sign up

Export Citation Format

Share Document