Search Results for author:
Found 10 papers, 7 papers with code
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.
Enriched Music Representations with Multiple Cross-modal Contrastive Learning
Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information.
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.
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.
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.
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.
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.
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.
