Deep learning model outperforms traditional methods in music mood prediction.
problem Predicting the emotional state of music from audio and lyrics.
method Implemented deep learning model alongside traditional feature engineering methods and compared their performance.
result Deep learning model outperforms traditional methods in arousal detection.
Study identifies personality traits from dance movements in music.
problem Predicting individual differences from music-induced movement.
method Identified Big Five personality traits and EQ/SQ scores from dance movements.
result Successfully explored unseen space for personality and EQ/SQ.
Detecting early signs of mood episodes in bipolar disorder patients.
problem Early identification of mood episodes in bipolar disorder patients for timely treatment.
method Signature-based model derived from stochastic analysis applied to real-time mood data.
result The signature method can identify the onset of mood episodes in bipolar disorder patients.
This study uses smartphone data to predict when mood interventions are needed for bipolar disorder.
problem Chronic mental illness with extreme mood changes that lead to personal or social consequences.
method Anomaly detection framework using Temporal Normalization to predict mood anomalies from natural speech data.
result A framework for real-world speech-focused mood monitoring using deep learning.
Machine learning identifies distinctive mood patterns in bipolar and borderline personality disorders.
problem Challenges in diagnosing bipolar and borderline personality disorders using retrospective mood recall.
method Signature-based machine learning model using daily mood ratings from smartphone apps.
result The model effectively separates participants into three groups with high accuracy.
Bayesian market views improve asset allocation performance.
problem Leveraging public mood for trusted and interpretable asset allocation.
method Formalize public mood into market views, use Bayesian asset allocation model, train neural models.
result Formalized market views increase portfolio profitability by 5-10% annually.
JamBot generates music with LSTMs, producing pleasant chord progressions and polyphonic music.
problem Generating harmonious and structured polyphonic music.
method Two-step process: chord LSTM for chord progression, second LSTM for polyphonic music generation.
result JamBot's learned chord embeddings reveal the circle of fifths, a significant finding in music theory.
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.
The paper categorizes music emotions and improves music retrieval.
problem Inefficient music retrieval based on album information.
method Categorical emotion expression, Fisher's separation theorem, feature extraction, Support Vector Machines.
result Maximum separability occurs between relaxing and epic music parts.
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.
The study analyzes sentiment of European tweets during the pandemic.
problem Understanding public sentiment during the COVID-19 pandemic.
method Cross-language sentiment analysis of multilingual tweets using neural networks and sentence embeddings.
result Sentiment analysis reveals that lockdown announcements correlate with a deterioration of mood, which recovers quickly.
Generative models learn from high-level music representations, but this work models music in raw audio.
problem Capturing long-range correlations in music generation.
method Exploring autoregressive discrete autoencoders (ADAs) to model raw audio waveforms.
result Unconditional generation of piano music in raw audio domain with long-range correlations.
Model learns to sort music clips in sequence.
problem Finding an optimal permutation of music clips.
method Proposed a music puzzle game for self-supervised learning of neural networks.
result Improved architecture (SEN) performs better on music medley.
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.
DSHL generates music using segment concatenation and hash learning.
problem Music generation using deep learning.
method Deep recurrent neural network and ranking-based hash learning.
result DSHL generates original and enjoyable music.
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.
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.
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.
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.
Paper proposes GAN models for generating multi-track music.
problem Generating music with multiple tracks and temporal dynamics.
method Generative adversarial networks (GANs) for multi-track music generation.
result Models can generate coherent music of four bars right from scratch.
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.
Universal music translation network across instruments and genres.
problem Translating music across different instruments, genres, and styles.
method Multi-domain wavenet autoencoder with a shared encoder and disentangled latent space trained end-to-end on waveforms.
result Achieves convincing translations even from domains not seen during training.
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.
Model creates a latent space for multitrack music measures.
problem Representing and exploring the structure of polyphonic music.
method Extended MusicVAE to a latent space, enabling various operations.
result Model can generate, interpolate, and manipulate musical measures.
Challenge aims to recognize music genres from audio.
problem Recognizing music genres from audio recordings.
method Open data challenge with submissions evaluated.
result Results presented from the challenge.
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.
A new VAE adds a classifier to generate music in different keys.
problem Generating music in different keys using VAEs.
method Classifying VAE with LSTM for sequential data.
result The Classifying VAE+LSTM models generate music in different keys better than non-classifying models.
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.
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.
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.