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Found 20 papers, 8 papers with code
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding
Efficiently compressing high-dimensional audio signals into a compact and informative latent space is crucial for various tasks, including generative modeling and music information retrieval (MIR).
Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation
Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive manner is also feasible.
Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach
In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts.
Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN
The generation of handwritten music sheets is a crucial step toward enhancing Optical Music Recognition (OMR) systems, which rely on large and diverse datasets for optimal performance.
Diff-MSTC: A Mixing Style Transfer Prototype for Cubase
Diff-MST, a deep learning model for mixing style transfer, forecasts mixing console parameters for tracks using a reference song.
Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation
This study emphasises the role of pixel-wise segmentation in advancing accurate music symbol recognition, contributing to knowledge discovery in OMR.
Music2Latent: Consistency Autoencoders for Latent Audio Compression
We demonstrate that Music2Latent outperforms existing continuous audio autoencoders in sound quality and reconstruction accuracy while achieving competitive performance on downstream MIR tasks using its latent representations.
MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models
Motivated by this, we introduce MuChoMusic, a benchmark for evaluating music understanding in multimodal language models focused on audio.
Diff-MST: Differentiable Mixing Style Transfer
Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song.
JAZZVAR: A Dataset of Variations found within Solo Piano Performances of Jazz Standards for Music Overpainting
In this paper, we outline the curation process for obtaining and sorting the repertoire, the pipeline for creating the Original and Variation pairs, and our analysis of the dataset.
Adoption of AI Technology in the Music Mixing Workflow: An Investigation
The integration of artificial intelligence (AI) technology in the music industry is driving a significant change in the way music is being composed, produced and mixed.
An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation
Results display a general phenomenon of over-fitting from two aspects, the pitch embedding space and the test loss of the single-token grid encoding.
Differentiable Time-Frequency Scattering on GPU
Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales.
The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging
The results highlight several important aspects of music tagging and neural networks.
Convolutional Recurrent Neural Networks for Music Classification
We introduce a convolutional recurrent neural network (CRNN) for music tagging.
Towards Music Captioning: Generating Music Playlist Descriptions
Descriptions are often provided along with recommendations to help users' discovery.
Explaining Deep Convolutional Neural Networks on Music Classification
Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e. g. genre classification, mood detection, and chord recognition.
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN).
Automatic tagging using deep convolutional neural networks
We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs).
Text-based LSTM networks for Automatic Music Composition
In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition.
