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Found 61 papers, 43 papers with code
Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation
Experimental results show that using binary neurons instead of HT or BS indeed leads to better results in a number of objective measures.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model.
Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions
In contrast with this general approach, this paper shows that Transformers can do even better for music modeling, when we improve the way a musical score is converted into the data fed to a Transformer model.
Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs
In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types.
Collaborative Similarity Embedding for Recommender Systems
We present collaborative similarity embedding (CSE), a unified framework that exploits comprehensive collaborative relations available in a user-item bipartite graph for representation learning and recommendation.
Ranked #1 on
Recommendation Systems
on MovieLens-Latest
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google's MelodyRNN models, each time using the same priming melody.
MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE
Transformers and variational autoencoders (VAE) have been extensively employed for symbolic (e. g., MIDI) domain music generation.
MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding
This paper presents an attempt to employ the mask language modeling approach of BERT to pre-train a 12-layer Transformer model over 4, 166 pieces of polyphonic piano MIDI files for tackling a number of symbolic-domain discriminative music understanding tasks.
Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer
To condition the generation process of such a model with a user-specified sequence, a popular approach is to take that conditioning sequence as a priming sequence and ask a Transformer decoder to generate a continuation.
Music Generation
Representation Learning
Sound
Multimedia
Audio and Speech Processing
The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures
This paper presents the Jazz Transformer, a generative model that utilizes a neural sequence model called the Transformer-XL for modeling lead sheets of Jazz music.
DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
In this work, we present DadaGP, a new symbolic music dataset comprising 26, 181 song scores in the GuitarPro format covering 739 musical genres, along with an accompanying tokenized format well-suited for generative sequence models such as the Transformer.
PerformanceNet: Score-to-Audio Music Generation with Multi-Band Convolutional Residual Network
To build such an AI performer, we propose in this paper a deep convolutional model that learns in an end-to-end manner the score-to-audio mapping between a symbolic representation of music called the piano rolls and an audio representation of music called the spectrograms.
Sound Multimedia Audio and Speech Processing
Pop Music Highlighter: Marking the Emotion Keypoints
In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs.
Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization
Audio examples, as well as the code for implementing our model, will be publicly available online upon paper publication.
Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network
A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs.
A Streamlined Encoder/Decoder Architecture for Melody Extraction
Our experiments on both vocal melody extraction and general melody extraction validate the effectiveness of the proposed model.
Relative Positional Encoding for Transformers with Linear Complexity
Recent advances in Transformer models allow for unprecedented sequence lengths, due to linear space and time complexity.
Speech-to-Singing Conversion in an Encoder-Decoder Framework
In this paper our goal is to convert a set of spoken lines into sung ones.
On Output Activation Functions for Adversarial Losses: A Theoretical Analysis via Variational Divergence Minimization and An Empirical Study on MNIST Classification
2) How different combinations of output activation functions and regularization approaches perform empirically against one another?
Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
Can we make a famous rap singer like Eminem sing whatever our favorite song?
Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding
This paper proposes a new self-attention based model for music score infilling, i. e., to generate a polyphonic music sequence that fills in the gap between given past and future contexts.
A Computational Analysis of Real-World DJ Mixes using Mix-To-Track Subsequence Alignment
A DJ mix is a sequence of music tracks concatenated seamlessly, typically rendered for audiences in a live setting by a DJ on stage.
Learning to match transient sound events using attentional similarity for few-shot sound recognition
In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition.
Sound Audio and Speech Processing
Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops
Music producers who use loops may have access to thousands in loop libraries, but finding ones that are compatible is a time-consuming process; we hope to reduce this burden with automation.
JukeDrummer: Conditional Beat-aware Audio-domain Drum Accompaniment Generation via Transformer VQ-VAE
Instead of generating the drum track directly as waveforms, we use a separate VQ-VAE to encode the mel-spectrogram of a drum track into another set of discrete codes, and train the Transformer to predict the sequence of drum-related discrete codes.
Score and Lyrics-Free Singing Voice Generation
Generative models for singing voice have been mostly concerned with the task of ``singing voice synthesis,'' i. e., to produce singing voice waveforms given musical scores and text lyrics.
Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation
We propose the BinaryGAN, a novel generative adversarial network (GAN) that uses binary neurons at the output layer of the generator.
Musical Composition Style Transfer via Disentangled Timbre Representations
We investigate disentanglement techniques such as adversarial training to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation (timbre) of the parts per short-time segment.
Audio and Speech Processing Sound
Compose & Embellish: Well-Structured Piano Performance Generation via A Two-Stage Approach
Even with strong sequence models like Transformers, generating expressive piano performances with long-range musical structures remains challenging.
A Minimal Template for Interactive Web-based Demonstrations of Musical Machine Learning
New machine learning algorithms are being developed to solve problems in different areas, including music.
Human-Computer Interaction D.2.2; H.5.2; H.5.5
The Freesound Loop Dataset and Annotation Tool
We present the Freesound Loop Dataset (FSLD), a new large-scale dataset of music loops annotated by experts.
Audio and Speech Processing Sound
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years.
Addressing the confounds of accompaniments in singer identification
A singer identification model may learn to extract non-vocal related features from the instrumental part of the songs, if a singer only sings in certain musical contexts (e. g., genres).
Source Separation-based Data Augmentation for Improved Joint Beat and Downbeat Tracking
Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years.
Learning To Generate Piano Music With Sustain Pedals
Recent years have witnessed a growing interest in research related to the detection of piano pedals from audio signals in the music information retrieval community.
Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation
Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities.
Weakly-supervised Visual Instrument-playing Action Detection in Videos
Instrument playing is among the most common scenes in music-related videos, which represent nowadays one of the largest sources of online videos.
Drum-Aware Ensemble Architecture for Improved Joint Musical Beat and Downbeat Tracking
This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals.
Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music
To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo.
Melody Infilling with User-Provided Structural Context
This paper proposes a novel Transformer-based model for music score infilling, to generate a music passage that fills in the gap between given past and future contexts.
Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss
A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public.
An Analysis Method for Metric-Level Switching in Beat Tracking
For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model.
Improving Automatic Jazz Melody Generation by Transfer Learning Techniques
In this paper, we tackle the problem of transfer learning for Jazz automatic generation.
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products
Vector-valued neural learning has emerged as a promising direction in deep learning recently.
Informed Group-Sparse Representation for Singing Voice Separation
Singing voice separation attempts to separate the vocal and instrumental parts of a music recording, which is a fundamental problem in music information retrieval.
Polar $n$-Complex and $n$-Bicomplex Singular Value Decomposition and Principal Component Pursuit
Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis.
Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation
Thus, in this letter, we extend principal component pursuit to the complex and quaternionic cases to account for the missing phase information.
Generating Music Medleys via Playing Music Puzzle Games
Generating music medleys is about finding an optimal permutation of a given set of music clips.
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant applications in the music industry.
Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals
Recent years have witnessed an increased interest in the application of persistent homology, a topological tool for data analysis, to machine learning problems.
Neural Network Based Next-Song Recommendation
The utilization of sequential patterns has boosted performance on several kinds of recommendation tasks.
Vertex-Context Sampling for Weighted Network Embedding
Many existing methods adopt a uniform sampling method to reduce learning complexity, but when the network is non-uniform (i. e. a weighted network) such uniform sampling incurs information loss.
Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation
In this work, we propose a denoising Auto-encoder with Recurrent skip Connections (ARC).
Dilated Convolution with Dilated GRU for Music Source Separation
To reach information at remote locations, we propose to combine dilated convolution with a modified version of gated recurrent units (GRU) called the `Dilated GRU' to form a block.
Automatic Melody Harmonization with Triad Chords: A Comparative Study
Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence.
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models
In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models.
Speech-to-Singing Conversion based on Boundary Equilibrium GAN
Specifically, given a speech input, and optionally the F0 contour of the target singing, the proposed model generates as the output a singing signal with a progressive-growing encoder/decoder architecture and boundary equilibrium GAN loss functions.
Automatic Composition of Guitar Tabs by Transformers and Groove Modeling
Deep learning algorithms are increasingly developed for learning to compose music in the form of MIDI files.
Sound Audio and Speech Processing
KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms
In this paper, we propose a novel neural network model called KaraSinger for a less-studied singing voice synthesis (SVS) task named score-free SVS, in which the prosody and melody are spontaneously decided by machine.
Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks
A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic with seamless transitions between adjacent tracks.
Music Score Expansion with Variable-Length Infilling
In this paper, we investigate using the variable-length infilling (VLI) model, which is originally proposed to infill missing segments, to "prolong" existing musical segments at musical boundaries.
