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Found 30 papers, 12 papers with code
High Resolution Guitar Transcription via Domain Adaptation
Focusing on the guitar, we refine this approach to training on score data using a dataset of commercially available score-audio pairs.
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged.
A Data-Driven Analysis of Robust Automatic Piano Transcription
Algorithms for automatic piano transcription have improved dramatically in recent years due to new datasets and modeling techniques.
Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing
Creating music is iterative, requiring varied methods at each stage.
Symbolic Music Representations for Classification Tasks: A Systematic Evaluation
Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i. e., music represented in terms of discrete note events) in an image-like or language like fashion.
A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion
We propose an alternative loss component in a loss function that is otherwise well-established among VC tasks, which has been shown to improve our model's SVC performance.
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model
Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mass of music archives.
Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio
In this paper, we show that the vanilla DSAE suffers from being sensitive to the choice of model architecture and capacity of the dynamic latent variables, and is prone to collapse the static latent variable.
A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization
Our method is also robust to structural differences between the performance and score sequences, which is a common limitation of standard alignment approaches.
MSTRE-Net: Multistreaming Acoustic Modeling for Automatic Lyrics Transcription
This paper makes several contributions to automatic lyrics transcription (ALT) research.
Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes
We also include ablation studies investigating the effects of the use of multiple kernel shapes and comparing different input representations for the audio and the note-related information.
Computational Pronunciation Analysis in Sung Utterances
Recent automatic lyrics transcription (ALT) approaches focus on building stronger acoustic models or in-domain language models, while the pronunciation aspect is seldom touched upon.
Structure-Aware Audio-to-Score Alignment using Progressively Dilated Convolutional Neural Networks
The identification of structural differences between a music performance and the score is a challenging yet integral step of audio-to-score alignment, an important subtask of music information retrieval.
Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation
This paper addresses the problem of domain adaptation for the task of music source separation.
Learning Frame Similarity using Siamese networks for Audio-to-Score Alignment
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece.
A Hybrid Approach to Audio-to-Score Alignment
Audio-to-score alignment aims at generating an accurate mapping between a performance audio and the score of a given piece.
Automatic Lyrics Transcription using Dilated Convolutional Neural Networks with Self-Attention
Speech recognition is a well developed research field so that the current state of the art systems are being used in many applications in the software industry, yet as by today, there still does not exist such robust system for the recognition of words and sentences from singing voice.
Reliable Local Explanations for Machine Listening
One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions.
Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling
In comparison to TCN and Wavenet, our network consistently saves memory and computation time, with speed-ups for training and inference of over 4x in the audio generation experiment in particular, while achieving a comparable performance in all tasks.
Ranked #2 on
Music Modeling
on Nottingham
Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
We apply our method to image generation, image segmentation and audio source separation, and obtain improved performance over a standard GAN when additional incomplete training examples are available.
GAN-based Generation and Automatic Selection of Explanations for Neural Networks
However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow.
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation
Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end.
Ranked #27 on
Music Source Separation
on MUSDB18
Jointly Detecting and Separating Singing Voice: A Multi-Task Approach
A main challenge in applying deep learning to music processing is the availability of training data.
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples.
Note Value Recognition for Piano Transcription Using Markov Random Fields
This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals.
An End-to-End Neural Network for Polyphonic Piano Music Transcription
We compare performance of the neural network based acoustic models with two popular unsupervised acoustic models.
A Hybrid Recurrent Neural Network For Music Transcription
We investigate the problem of incorporating higher-level symbolic score-like information into Automatic Music Transcription (AMT) systems to improve their performance.
Identifying Cover Songs Using Information-Theoretic Measures of Similarity
This paper investigates methods for quantifying similarity between audio signals, specifically for the task of of cover song detection.
Sequential Complexity as a Descriptor for Musical Similarity
To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks.
