1.4k citations · 1.7k across the 24 of their papers we have counts for
16 papers · 1 filter
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries
Julia Wilkins, Justin Salamon, Magdalena Fuentes +2
Finding the right sound effects (SFX) to match moments in a video is a difficult and time-consuming task, and relies heavily on the quality and completeness of text metadata. Retri…
Audio-Text Models Do Not Yet Leverage Natural Language
Ho-Hsiang Wu, Oriol Nieto, Juan Pablo Bello +1
Multi-modal contrastive learning techniques in the audio-text domain have quickly become a highly active area of research. Most works are evaluated with standard audio retrieval an…
HEAR: Holistic Evaluation of Audio Representations
Joseph Turian, Jordie Shier, Humair Raj Khan +20
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to dev…
Who calls the shots? Rethinking Few-Shot Learning for Audio
Yu Wang, Nicholas J. Bryan, Justin Salamon +2
Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advance…
Soundata: A Python library for reproducible use of audio datasets
Magdalena Fuentes, Justin Salamon, Pablo Zinemanas +6
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 reprodu…
What's All the FUSS About Free Universal Sound Separation Data?
Scott Wisdom, Hakan Erdogan, Daniel Ellis +6
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound typ…
Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes
Nicolas Turpault, Romain Serizel, Scott Wisdom +5
We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We anal…
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez +9
We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems fo…
Metric Learning vs Classification for Disentangled Music Representation Learning
Jongpil Lee, Nicholas J. Bryan, Justin Salamon +2
Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two centr…
Few-Shot Drum Transcription in Polyphonic Music
Yu Wang, Justin Salamon, Mark Cartwright +2
Data-driven approaches to automatic drum transcription (ADT) are often limited to a predefined, small vocabulary of percussion instrument classes. Such models cannot recognize out-…
Improving Sound Event Detection In Domestic Environments Using Sound Separation
Nicolas Turpault, Scott Wisdom, Hakan Erdogan +5
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or nois…
Robust sound event detection in bioacoustic sensor networks
Vincent Lostanlen, Justin Salamon, Andrew Farnsworth +2
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving…
SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollution
Juan Pablo Bello, Claudio Silva, Oded Nov +5
We present the Sounds of New York City (SONYC) project, a smart cities initiative focused on developing a cyber-physical system for the monitoring, analysis and mitigation of urban…
Adaptive pooling operators for weakly labeled sound event detection
Brian McFee, Justin Salamon, Juan Pablo Bello
Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine le…
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Justin Salamon, Juan Pablo Bello
The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the…
The Implementation of Low-cost Urban Acoustic Monitoring Devices
Charlie Mydlarz, Justin Salamon, Juan Pablo Bello
The urban sound environment of New York City (NYC) can be, amongst other things: loud, intrusive, exciting and dynamic. As indicated by the large majority of noise complaints regis…