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Found 55 papers, 40 papers with code
Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio
APNet allows prototypes' reconstruction to waveforms for interpretability relying on the nearest training data samples.
WikiMuTe: A web-sourced dataset of semantic descriptions for music audio
The model is evaluated on two tasks: tag-based music retrieval and music auto-tagging.
ChoralSynth: Synthetic Dataset of Choral Singing
Choral singing, a widely practiced form of ensemble singing, lacks comprehensive datasets in the realm of Music Information Retrieval (MIR) research, due to challenges arising from the requirement to curate multitrack recordings.
Data leakage in cross-modal retrieval training: A case study
In our experiments, we find that the new splits serve as a more challenging benchmark.
FlowGrad: Using Motion for Visual Sound Source Localization
Most recent work in visual sound source localization relies on semantic audio-visual representations learned in a self-supervised manner, and by design excludes temporal information present in videos.
Matching Text and Audio Embeddings: Exploring Transfer-learning Strategies for Language-based Audio Retrieval
We present an analysis of large-scale pretrained deep learning models used for cross-modal (text-to-audio) retrieval.
Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification
Prototype Generation (PG) methods are typically considered for improving the efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling high-size corpora.
Emotion Embedding Spaces for Matching Music to Stories
Content creators often use music to enhance their stories, as it can be a powerful tool to convey emotion.
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning
Having attracted attention only recently, very few works on AAC study the performance of existing pre-trained audio and natural language processing resources.
Soundata: A Python library for reproducible use of audio datasets
Soundata is a Python library for loading and working with audio datasets in a standardized way, removing the need for writing custom loaders in every project, and improving reproducibility by providing tools to validate data against a canonical version.
Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks
Recent studies have put into question the commonly assumed shift invariance property of convolutional networks, showing that small shifts in the input can affect the output predictions substantially.
LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters
Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production.
Self-Supervised Learning from Automatically Separated Sound Scenes
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings.
Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging
We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649, 091tracks and 148, 826 associated playlists annotated by 30, 652 different tags.
Unsupervised Contrastive Learning of Sound Event Representations
Self-supervised representation learning can mitigate the limitations in recognition tasks with few manually labeled data but abundant unlabeled data---a common scenario in sound event research.
Multimodal Metric Learning for Tag-based Music Retrieval
In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings.
Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags
In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embeddings model (WEM), and a multi-head self-attention (MHA) mechanism.
FSD50K: An Open Dataset of Human-Labeled Sound Events
Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on over 2M tracks from YouTube videos and encompassing over 500 sound classes.
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
Exploring Longitudinal Effects of Session-based Recommendations
Specifically, we analyze to what extent algorithms of different types may lead to concentration effects over time.
COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations
Audio representation learning based on deep neural networks (DNNs) emerged as an alternative approach to hand-crafted features.
Evaluation of CNN-based Automatic Music Tagging Models
Recent advances in deep learning accelerated the development of content-based automatic music tagging systems.
Ranked #1 on
Music Auto-Tagging
on MagnaTagATune (clean)
Music Auto-Tagging
Audio and Speech Processing
Sound
Addressing Missing Labels in Large-Scale Sound Event Recognition Using a Teacher-Student Framework With Loss Masking
The study of label noise in sound event recognition has recently gained attention with the advent of larger and noisier datasets.
Search Result Clustering in Collaborative Sound Collections
In our work, we propose a graph-based approach using audio features for clustering diverse sound collections obtained when querying large online databases.
TensorFlow Audio Models in Essentia
Essentia is a reference open-source C++/Python library for audio and music analysis.
Neural Percussive Synthesis Parameterised by High-Level Timbral Features
We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds.
How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging
Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advances in deep learning.
Artist and style exposure bias in collaborative filtering based music recommendations
Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles.
Visualizing and Understanding Self-attention based Music Tagging
Recently, we proposed a self-attention based music tagging model.
Sound Audio and Speech Processing
Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers
We show that these simple methods can be effective in mitigating the effect of label noise, providing up to 2. 5\% of accuracy boost when incorporated to two different CNNs, while requiring minimal intervention and computational overhead.
musicnn: Pre-trained convolutional neural networks for music audio tagging
Pronounced as "musician", the musicnn library contains a set of pre-trained musically motivated convolutional neural networks for music audio tagging: https://github. com/jordipons/musicnn.
A hybrid parametric-deep learning approach for sound event localization and detection
This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge.
Data Augmentation for Instrument Classification Robust to Audio Effects
Reusing recorded sounds (sampling) is a key component in Electronic Music Production (EMP), which has been present since its early days and is at the core of genres like hip-hop or jungle.
Sound Audio and Speech Processing
Toward Interpretable Music Tagging with Self-Attention
In addition, we demonstrate the interpretability of the proposed architecture with a heat map visualization.
Sound Audio and Speech Processing
Audio tagging with noisy labels and minimal supervision
The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes.
Skip prediction using boosting trees based on acoustic features of tracks in sessions
The Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not.
Learning Sound Event Classifiers from Web Audio with Noisy Labels
To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42. 5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data.
Facilitating the Manual Annotation of Sounds When Using Large Taxonomies
It enables, for instance, the development of automatic tools for the annotation of large and diverse multimedia collections.
End-to-end music source separation: is it possible in the waveform domain?
Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase.
Ranked #26 on
Music Source Separation
on MUSDB18
Training neural audio classifiers with few data
We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections.
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline
The goal of the task is to build an audio tagging system that can recognize the category of an audio clip from a subset of 41 diverse categories drawn from the AudioSet Ontology.
Natural Language Processing for Music Knowledge Discovery
Today, a massive amount of musical knowledge is stored in written form, with testimonies dated as far back as several centuries ago.
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine.
Towards an efficient deep learning model for musical onset detection
We first review the state-of-the-art deep learning models for MOD, and identify their shortcomings and challenges: (i) the lack of hyper-parameter tuning details, (ii) the non-availability of code for training models on other datasets, and (iii) ignoring the network capability when comparing different architectures.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
In the second step, the syllable and phoneme boundaries and labels are inferred hierarchically by using a duration-informed hidden Markov model (HMM).
Sound Information Retrieval Audio and Speech Processing
Transfer Learning of Artist Group Factors to Musical Genre Classification
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy.
Randomly weighted CNNs for (music) audio classification
The computer vision literature shows that randomly weighted neural networks perform reasonably as feature extractors.
Sound Audio and Speech Processing
End-to-end learning for music audio tagging at scale
The lack of data tends to limit the outcomes of deep learning research, particularly when dealing with end-to-end learning stacks processing raw data such as waveforms.
Sound Audio and Speech Processing
Metrical-accent Aware Vocal Onset Detection in Polyphonic Audio
The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings.
Audio to score matching by combining phonetic and duration information
We approach the singing phrase audio to score matching problem by using phonetic and duration information - with a focus on studying the jingju a cappella singing case.
Sound
A Deep Multimodal Approach for Cold-start Music Recommendation
Second, track embeddings are learned from the audio signal and available feedback data.
A Wavenet for Speech Denoising
In order to overcome this limitation, we propose an end-to-end learning method for speech denoising based on Wavenet.
Sound
Timbre Analysis of Music Audio Signals with Convolutional Neural Networks
The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms.
Sound
ELMD: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain
In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain.
