The First Cadenza Signal Processing Challenge: Improving Music for Those With a Hearing Loss

The Cadenza project aims to improve the audio quality of music for those who have a hearing loss. This is being done through a series of signal processing challenges, to foster better and more inclusive technologies. In the first round, two common listening scenarios are considered: listening to music over headphones, and with a hearing aid in a car. The first scenario is cast as a demixing-remixing problem, where the music is decomposed into vocals, bass, drums and other components. These can then be intelligently remixed in a personalized way, to increase the audio quality for a person who has a hearing loss. In the second scenario, music is coming from car loudspeakers, and the music has to be enhanced to overcome the masking effect of the car noise. This is done by taking into account the music, the hearing ability of the listener, the hearing aid and the speed of the car. The audio quality of the submissions will be evaluated using the Hearing Aid Audio Quality Index (HAAQI) for objective assessment and by a panel of people with hearing loss for subjective evaluation.

The Cadenza ICASSP 2024 Grand Challenge

The Cadenza project aims to enhance the audio quality of music for individuals with hearing loss. As part of this, the project is organizing the ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The challenge can be tackled by decomposing the music at the hearing aid microphones into vocals, bass, drums, and other components. These can then be intelligently remixed in a personalized manner to improve audio quality. Alternatively, an end-to-end approach could be used. Processes need to consider the music itself, the gain applied to each component, and the listener's hearing loss. The submitted entries will be evaluated using the intrusive objective metric, the Hearing Aid Audio Quality Index (HAAQI). This paper outlines the challenge.

The Use of Voice Source Features for Sung Speech Recognition

In this paper, we ask whether vocal source features (pitch, shimmer, jitter, etc) can improve the performance of automatic sung speech recognition, arguing that conclusions previously drawn from spoken speech studies may not be valid in the sung speech domain. We first use a parallel singing/speaking corpus (NUS-48E) to illustrate differences in sung vs spoken voicing characteristics including pitch range, syllables duration, vibrato, jitter and shimmer. We then use this analysis to inform speech recognition experiments on the sung speech DSing corpus, using a state of the art acoustic model and augmenting conventional features with various voice source parameters. Experiments are run with three standard (increasingly large) training sets, DSing1 (15.1 hours), DSing3 (44.7 hours) and DSing30 (149.1 hours). Pitch combined with degree of voicing produces a significant decrease in WER from 38.1% to 36.7% when training with DSing1 however smaller decreases in WER observed when training with the larger more varied DSing3 and DSing30 sets were not seen to be statistically significant. Voicing quality characteristics did not improve recognition performance although analysis suggests that they do contribute to an improved discrimination between voiced/unvoiced phoneme pairs.