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.
Framework generates pop song melodies and piano accompaniment.
A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated music composers able to gen…
Generates music with coherent rhythm, chords, and melody using LSTM models.
Deep learning model estimates multiple f0s, melodies, vocals, and bass lines from music.
The musical notes from a hyperbolic marimba can identify the shape of hyperbolic surfaces.
Improved cover detection using dominant melody embeddings.
Users create melodies with AI, harmonized in the style of Bach.
Modelling the real world complexity of music is a challenge for machine learning. We address the task of modeling melodic sequences from the same music genre. We perform a comparative analysis of two probabilistic models; a Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional Restricted Boltz…
This work adapts RDT for mental program construction, showing benefits and costs.
Unified framework analyzes and compares RFF and RoPE PEs for music generation.
We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of using the body interactions of order Markov models, traditionally used in automatic mus…
Improved cover detection in music datasets with novel triplet loss.
We examine the problem of learning a probabilistic model for melody directly from musical sequences belonging to the same genre. This is a challenging task as one needs to capture not only the rich temporal structure evident in music, but also the complex statistical dependencies among different music components. To ad…
Transformer autoencoder learns musical style from performances.
Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for int…
Classification of sequence data is the topic of interest for dynamic Bayesian models and Recurrent Neural Networks (RNNs). While the former can explicitly model the temporal dependencies between class variables, the latter have a capability of learning representations. Several attempts have been made to improve perform…
System composes polyphonic music using LSTM and RL.
Transformer model generates long musical compositions with compelling structure.
ragamAI uses machine learning to create concert recitals for Carnatic music.
Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments/tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical no…
Paper proposes LSTM for automatic music generation.