A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper compares XGB and BPNN for music style classification.
New system for automatic music emotion recognition considers multiple emotions simultaneously.
Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarit…
SVM classifier outperforms other models in classifying musical instruments from IRMAS data.
Graph neural networks improve music genre classification on audio datasets.
Neural nets classify Thai Lukthung songs from other genres.
This paper proposes a method for instrument classification in polyphonic music using monophonic data.
Paper classifies Brazilian music genres using song lyrics with BLSTM network.
A model classifies music genres from MP3 files using metric learning and feature extraction.
This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annot…
Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This pa…
Music genre can be hard to describe: many factors are involved, such as style, music technique, and historical context. Some genres even have overlapping characteristics. Looking for a better understanding of how music genres are related to musical harmonic structures, we gathered data about the music chords for thousa…
End-to-end models classify composers from musical scores.
This paper explores deep learning in music generation, from history to current techniques.
Low-dimensional vectors improve semantic understanding of music and language.
Neural network generates music scores directly from polyphonic audio.
Bardo Composer generates tabletop RPG music based on player speech.
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms. Such interest has grown in the area of music information retrieval (MIR) as well, particularly in music audio classification tasks such as auto…
New method predicts target genres from source genres, unifying music tag systems.
Paper tackles musical version matching at segment level using contrastive learning from weakly-labeled data.
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no lo…
Improved cover detection in music datasets with novel triplet loss.
Speech, Music and Noise classification/segmentation is an important preprocessing step for audio processing/indexing. To this end, we propose a novel 1D Convolutional Neural Network (CNN) - SwishNet. It is a fast and lightweight architecture that operates on MFCC features which is suitable to be added to the front-end …
Principal component analysis (PCA) is a popular method for projecting data onto uncorrelated components in lower dimension, although the optimal number of components is not specified. Likewise, multiple signal classification (MUSIC) algorithm is a popular PCA-based method for estimating directions of arrival (DOAs) of …
MuLan links music audio to natural language tags.
Model generates music to connect missing parts, leveraging latent space of VAE.
Trains word embeddings from music and text data to link music contexts.
Proposes COALA method for learning audio representations aligned with tags.
Music SketchNet generates missing measures in incomplete music pieces, guided by user input.
Dual-track model generates classical piano music better than other methods.
A model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. Detailed in the paper is a deep reinforcement learning architecture that predicts and generates polyphonic music aligned with musical rules. The probabilistic model presented is a Bi-axial LS…
We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harm…
Recently, digital music libraries have been developed and can be plainly accessed. Latest research showed that current organization and retrieval of music tracks based on album information are inefficient. Moreover, they demonstrated that people use emotion tags for music tracks in order to search and retrieve them. In…
Adversarial autoencoder improves music latent space learning.
As deep learning advances, algorithms of music composition increase in performance. However, most of the successful models are designed for specific musical structures. Here, we present BachProp, an algorithmic composer that can generate music scores in many styles given sufficient training data. To adapt BachProp to a…
Music generation research has grown in popularity over the past decade, thanks to the deep learning revolution that has redefined the landscape of artificial intelligence. In this paper, we propose a novel approach to music generation inspired by musical segment concatenation methods and hash learning algorithms. Given…
Bi-LSTM with attention generates jazz music with rich nuances.
Generative model creates realistic dance poses from music.
Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sor…
This research improves neural synthesizers for music sounds from speech data.
Anticipatory model generates music with control over events.
We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of handling polyphonic music with multiple instrument tracks, as well as modeling the dynamics of music by incorporating note durations and velocities. We show that MIDI-VAE can perform style transfer on symbolic music by au…
We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newl…
The paper models musical motif transformations in Beethoven's works.
EC^2-VAE generates music analogies by disentangling pitch and rhythm representations.
In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability of recurrent neural networks to learn complex temporal patterns lends itself well to the musical domain. Promising results have been observe…
The study assesses music as an investment asset class using discounted cashflow models.
ragamAI uses machine learning to create concert recitals for Carnatic music.