DeepDrum generates drum rhythms under musical constraints.
problem Generating rhythms that adhere to musical style and constraints.
method Adaptive Neural Network with Conditional Layers containing musical parameters and instrumentation.
result DeepDrum effectively generates rhythms that resemble learned styles while conforming to given constraints.
Improves music composition with user-defined constraints using continuous time models.
problem Combining sequence models with user-defined constraints in continuous time.
method Introduces a novel particle filter scheme for continuous time point processes.
result The particle filter scheme yields superior results in a human listening test.
MUSIC learns coupled systems with sparse data and incomplete physics.
problem Learning coupled systems with incomplete physical constraints and missing data.
method Sparsity induced multitask neural network framework integrating partial physical constraints with data-driven learning.
result MUSIC accurately learns solutions to complex coupled systems under data-scarce and noisy conditions.
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…
Transformer improves pop piano composition by incorporating beat-based structure.
problem Generating expressive pop piano compositions with coherent rhythmic structure.
method Improved data representation for Transformers, incorporating beat-bar-phrase structure.
result Composes pop piano music with better rhythmic structure than existing models.
We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model. The acoustic model is a neural network used for estimating the probabilities of pitches in …
Improved cover detection in music datasets with novel triplet loss.
problem Challenging task of automatically detecting covers in audio datasets.
method Convolutional neural network mapping melodic features to embeddings, training to minimize cover distance and maximize non-cover distance.
result New prototypical triplet loss improves accuracy for large datasets and live songs.
Piano Genie lets non-musicians improvise on a piano.
problem Improvising on the piano for non-musicians.
method Trained recurrent neural network autoencoders with discrete bottlenecks to learn mappings between user inputs and piano pieces.
result Piano Genie allows users to improvise on the piano with musically meaningful constraints.
Synthesizing human's movements such as dancing is a flourishing research field which has several applications in computer graphics. Recent studies have demonstrated the advantages of deep neural networks (DNNs) for achieving remarkable performance in motion and music tasks with little effort for feature pre-processing.…
Model generates coherent polyphonic music using deep reinforcement learning.
problem Creating music that follows musical rules and coherence.
method Deep reinforcement learning architecture with a Bi-axial LSTM trained with a pseudo-kernel and DQN for exploration and coherence.
result The model generates polyphonic music that performs well quantitatively and qualitatively.
New system for automatic music emotion recognition considers multiple emotions simultaneously.
problem Automatic recognition of simultaneous and multiplicity of emotions in music.
method Comparison of multilabel and multiclass machine learning algorithms on the Emotify dataset.
result The Geneva Emotional Music Scale 9 is adopted for multilabel and multiclass classification of music emotions.
MuLan links music audio to natural language tags.
problem Traditional music tagging systems use rigid attributes; MuLan aims to link audio directly to natural language.
method Joint audio-text embedding model trained on 44 million music recordings and text annotations.
result MuLan's embeddings enable zero-shot functionalities and transfer learning.
Model generates music to connect missing parts, leveraging latent space of VAE.
problem Music inpainting from missing or lost information.
method Deep learning model using VAE latent space and RNN to traverse latent space conditioned on past and future musical contexts.
result Model generates meaningful music inpaintings, connecting musical excerpts.
MIDI-VAE models music dynamics and instrumentation for style transfer.
problem Modeling and transferring musical style across different instruments and dynamics.
method Variational Autoencoder (VAE) for polyphonic music modeling and style transfer.
result MIDI-VAE successfully transfers musical style between different genres and instruments.
BachProp generates music in various styles using deep learning.
problem Creating music algorithms that can adapt to multiple styles.
method Developed a novel music representation and trained a deep network to predict note transitions.
result BachProp generates music scores that better capture features of original corpora.
Trains word embeddings from music and text data to link music contexts.
problem Varying vocabulary size and musical relevance in word embeddings.
method Combines general text and music-specific data to train word embeddings.
result Trained embeddings better associate music contexts with compositions.
Music SketchNet generates missing measures in incomplete music pieces, guided by user input.
problem Generating missing measures in incomplete monophonic musical pieces.
method Introducing SketchVAE for factorized representation of rhythm and pitch, and two discriminative architectures for guided music completion.
result Our approach outperforms state-of-the-art models in both objective and subjective evaluations.
Dual-track model generates classical piano music better than other methods.
problem Generating inter-dependent piano music.
method Dual-track architecture with various neural network models and music representations.
result Proposed model outperforms other methods in evaluation.
System composes polyphonic music using LSTM and RL.
problem Complex polyphonic music composition.
method Divided music into monophonic streams, trained LSTM to generate sequences, used RL to find pleasant compositions.
result System generates intricate melodies, chords, and contrapuntal sequences.
The study uses music chords to predict Brazilian music genres.
problem Classifying popular Brazilian music genres based on harmonic structures.
method Extracted and engineered harmonically related features from chords data, used random forest model for classification.
result Features from harmonic elements can predict Brazilian music genres.
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…
Word2Vec captures musical relationships in complex polyphonic music.
problem Capturing meaningful relationships in musical contexts.
method Skip-gram version of word2vec applied to music slices from a large corpus.
result Word2Vec embeddings reveal functional chord and harmonic associations.
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.
problem Learning effective latent spaces for symbolic music data.
method Adversarial regularization with Gaussian mixtures.
result MusAE outperforms standard VAEs in reconstruction and interpolation.
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.
problem Challenges in producing realistic music with structure and rationality.
method Deep learning, Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with Attention.
result Bi-LSTMs with attention preserve the richness and technical nuances of old style music.
Paper introduces a new model for polyphonic music composition.
problem Creating music with multiple interwoven voices.
method Developed a coupled recurrent model using probabilistic factorization and neural network ideas.
result Trained models for single-voice and multi-voice composition on a large dataset.
Generative model creates realistic dance poses from music.
problem Generating human-like dance poses from music.
method Music feature encoder, pose generator, music genre classifier integrated.
result Generative autoregressive model synthesizes dance sequences up to 5,000 frames.
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…
Paper applies GANs to symbolic music genre transfer.
problem Symbolic music genre transfer using GANs.
method CycleGAN architecture with additional discriminators to preserve structure.
result Fidelity of transformed music improved with additional discriminators.
This research improves neural synthesizers for music sounds from speech data.
problem Applying speech synthesis techniques to musical instrument sounds.
method Comparison of three neural synthesizers in three scenarios: training, zero-shot learning, and fine-tuning.
result Neural synthesizers trained on speech data and fine-tuned on music data perform better.
Anticipatory model generates music with control over events.
problem Controlling symbolic music generation.
method Interleaving event and control sequences to predict future events.
result Anticipatory model matches autoregressive models in performance and can infill control tasks.
New CRF model segments music into chords with rich features.
problem Harmonic analysis of music segments.
method Semi-Markov Conditional Random Field (semi-CRF) with segment-level features.
result Semi-CRF model performs better than previous approaches.
The paper models musical motif transformations in Beethoven's works.
problem Understanding how motifs transform in symbolic music.
method Developed a probabilistic framework using Conditional Random Fields.
result Identified patterns of motif transformations and their co-occurrences.
EC^2-VAE generates music analogies by disentangling pitch and rhythm representations.
problem Disentangling music representations for generating creative analogies.
method Explicitly-constrained variational autoencoder (EC^2-VAE) for disentangling pitch and rhythm representations.
result EC^2-VAE enables the generation of music analogies by borrowing representations from different pieces.
Paper compares XGB and BPNN for music style classification.
problem Efficient music style classification using different methods.
method Feature extraction for timbral texture, rhythmic content, and pitch content; comparative evaluation of XGB and BPNN.
result XGB outperforms BPNN for small datasets in music classification.
New dataset and models generate piano music with coherent structure across multiple timescales.
problem Generating coherent musical structure with neural networks is challenging.
method Used notes as an intermediate representation to model and synthesize music across multiple timescales.
result Trained models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure.
The study assesses music as an investment asset class using discounted cashflow models.
problem Quantifying the risk and return characteristics of music royalty assets.
method Fitting three discounted cashflow models to Royalty Exchange platform transactions and backtesting performance.
result Life of Rights music assets had risk and return characteristics comparable to stocks in the S\&P500 over 5 years.
ragamAI uses machine learning to create concert recitals for Carnatic music.
problem Creating a comprehensive listening experience for Carnatic music concerts.
method Playlist and session-based recommender models, leveraging mathematical structure in past concerts.
result ragamAI generates concert recitals that perform 25%-50% better than baseline models.
Study develops supervised algorithm for symbolic music style translation.
problem Transforming musical style while maintaining original content.
method Synthetic data generation for aligned training data, encoder-decoder model.
result Trained models produce meaningful accompaniments for real MIDI recordings.
Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musical…
Paper proposes LSTM for automatic music generation.
problem Generating music without human intervention.
method Recurrent Neural Networks (RNN), specifically LSTM.
result Model generates new melodies with high accuracy.
FIGARO generates symbolic music with fine-grained control.
problem Minimal control over generated music sequences.
method Description-to-sequence task, learning conditional distribution of sequences given high-level descriptions.
result State-of-the-art controllable symbolic music generation.
A new neural network separates vocals from music accompaniment.
problem Separating vocals from music accompaniment in recordings.
method Self-attention convolutional neural network (CNN) with densely-connected blocks.
result 19.5% relative improvement in vocals separation.
Real music signals are highly variable, yet they have strong statistical structure. Prior information about the underlying physical mechanisms by which sounds are generated and rules by which complex sound structure is constructed (notes, chords, a complete musical score), can be naturally unified using Bayesian modell…
We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-ind…
Graph neural networks improve music genre classification on audio datasets.
problem Difficulty in applying deep learning on spectrograms due to lack of quality data and augmentation.
method Combination of CNN and Graph Neural Networks (GNN) with Siamese Neural Networks.
result Achieved state-of-the-art results on GTZAN and AudioSet datasets.
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…