Timbre analysis of music audio signals with convolutional neural networks
The focus of this work is to study how to efficiently tailor Convolutional Neural Networks
(CNNs) towards learning timbre representations from log-mel magnitude spectrograms. We …
(CNNs) towards learning timbre representations from log-mel magnitude spectrograms. We …
Foundation models for music: A survey
In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …
FMA: A dataset for music analysis
We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable
for evaluating several tasks in MIR, a field concerned with browsing, searching, and …
for evaluating several tasks in MIR, a field concerned with browsing, searching, and …
Music recommendation systems: Techniques, use cases, and challenges
This chapter gives an introduction to music recommender systems, considering the unique
characteristics of the music domain. We take a user-centric perspective, by organizing our …
characteristics of the music domain. We take a user-centric perspective, by organizing our …
Musical trends and predictability of success in contemporary songs in and out of the top charts
We analyse more than 500 000 songs released in the UK between 1985 and 2015 to
understand the dynamics of success (defined as 'making it'into the top charts), correlate …
understand the dynamics of success (defined as 'making it'into the top charts), correlate …
Machine learning for music genre: multifaceted review and experimentation with audioset
Music genre classification is one of the sub-disciplines of music information retrieval (MIR)
with growing popularity among researchers, mainly due to the already open challenges …
with growing popularity among researchers, mainly due to the already open challenges …
[PDF][PDF] Hit Song Prediction: Leveraging Low-and High-Level Audio Features.
Assessing the potential success of a given song based on its acoustic characteristics is an
important task in the music industry. This task has mostly been approached from an internal …
important task in the music industry. This task has mostly been approached from an internal …
[PDF][PDF] Music genre classification using machine learning algorithms: a comparison
S Chillara, AS Kavitha, SA Neginhal, S Haldia… - Int Res J Eng …, 2019 - academia.edu
Music plays a very important role in people's lives. Music bring like-minded people together
and is the glue that holds communities together. Communities can be recognized by the type …
and is the glue that holds communities together. Communities can be recognized by the type …
Saraga: open datasets for research on indian art music
We introduce two large open data collections of Indian Art Music, both its Carnatic and
Hindustani traditions, comprising audio from vocal concerts, editorial metadata, and time …
Hindustani traditions, comprising audio from vocal concerts, editorial metadata, and time …
Tensorflow audio models in essentia
Essentia is a reference open-source C++/Python library for audio and music analysis. In this
work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions …
work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions …