Search Results for author:
Found 8 papers, 4 papers with code
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses
In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media.
A dataset and classification model for Malay, Hindi, Tamil and Chinese music
We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups.
The impact of Audio input representations on neural network based music transcription
This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription.
Sound Audio and Speech Processing
nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks
First, it takes a lot of hard disk space to store different frequency domain representations.
Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion.
Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders
Specifically, we use two separate encoders to learn distinct latent spaces for timbre and pitch, which form Gaussian mixture components representing instrument identity and pitch, respectively.
From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec
In this newly learned vector space, a metric based on cosine distance is able to distinguish between functional chord relationships, as well as harmonic associations in the music.
From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models
Tonal structure is in part conveyed by statistical regularities between musical events, and research has shown that computational models reflect tonal structure in music by capturing these regularities in schematic constructs like pitch histograms.
