Search Results for author:
Found 8 papers, 6 papers with code
A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models
In summary, this work contributes to the understanding of the robustness of CNN-LSTM models, particularly in SER scenarios, and the impact of AEs.
Deep Feature Learning for Medical Acoustics
The purpose of this paper is to compare different learnable frontends in medical acoustics tasks.
Variational Autoencoders for Anomaly Detection in Respiratory Sounds
This paper proposes a weakly-supervised machine learning-based approach aiming at a tool to alert patients about possible respiratory diseases.
Acoustics-specific Piano Velocity Estimation
This is due to 1) the different mappings between MIDI parameters used by different instruments, and 2) the fact that musicians adapt their way of playing to the surrounding acoustic environment.
A Perceptual Measure for Evaluating the Resynthesis of Automatic Music Transcriptions
This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change.
ASMD: an automatic framework for compiling multimodal datasets with audio and scores
This paper describes an open-source Python framework for handling datasets for music processing tasks, built with the aim of improving the reproducibility of research projects in music computing and assessing the generalization abilities of machine learning models.
Multimedia Digital Libraries Sound Audio and Speech Processing H.5.5; I.2.m; H.3.7; J.5
A Convolutional Approach to Melody Line Identification in Symbolic Scores
The backbone of the method consists of a convolutional neural network (CNN) estimating the probability that each note in the score (more precisely: each pixel in a piano roll encoding of the score) belongs to the melody line.
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music.
