Localizing a moving sound source in the real world involves determining ...
Finding the right sound effects (SFX) to match moments in a video is a
d...
Multi-modal contrastive learning techniques in the audio-text domain hav...
Most recent work in visual sound source localization relies on semantic
...
Deep learning-based approaches to musical source separation are often li...
Localizing visual sounds consists on locating the position of objects th...
Audio applications involving environmental sound analysis increasingly u...
The Sounds of New York City (SONYC) wireless sensor network (WSN) has be...
Few-shot learning aims to train models that can recognize novel classes ...
Soundata is a Python library for loading and working with audio datasets...
While the estimation of what sound sources are, when they occur, and fro...
Deep learning is very data hungry, and supervised learning especially
re...
We present SONYC-UST-V2, a dataset for urban sound tagging with
spatiote...
Data-driven approaches to automatic drum transcription (ADT) are often
l...
With the aim of constructing a biologically plausible model of machine
l...
This paper proposes to perform unsupervised detection of bioacoustic eve...
To explain the consonance of octaves, music psychologists represent pitc...
Automatic music transcription is considered to be one of the hardest pro...
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs...
Noise pollution is one of the topmost quality of life issues for urban
r...
The recent success of raw audio waveform synthesis models like WaveNet
m...
We present the Sounds of New York City (SONYC) project, a smart cities
i...
Sound event detection (SED) methods are tasked with labeling segments of...
The task of estimating the fundamental frequency of a monophonic sound
r...
The ability of deep convolutional neural networks (CNN) to learn
discrim...