Search Results for author:
Found 17 papers, 8 papers with code
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries
We show that our system, trained using our automatic data curation pipeline, significantly outperforms baselines trained on in-the-wild data on the task of HQ SFX retrieval for video.
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.
A Study on Robustness to Perturbations for Representations of Environmental Sound
To accomplish this, we imitate channel effects by injecting perturbations to the audio signal and measure the shift in the new (perturbed) embeddings with three distance measures, making the evaluation domain-dependent but not task-dependent.
Wav2CLIP: Learning Robust Audio Representations From CLIP
We propose Wav2CLIP, a robust audio representation learning method by distilling from Contrastive Language-Image Pre-training (CLIP).
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.
Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes
While the estimation of what sound sources are, when they occur, and from where they originate has been well-studied, the estimation of how loud these sound sources are has been often overlooked.
Multi-Task Self-Supervised Pre-Training for Music Classification
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags.
One or Two Components? The Scattering Transform Answers
With the aim of constructing a biologically plausible model of machine listening, we study the representation of a multicomponent stationary signal by a wavelet scattering network.
Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization
This paper proposes to perform unsupervised detection of bioacoustic events by pooling the magnitudes of spectrogram frames after per-channel energy normalization (PCEN).
Learning the helix topology of musical pitch
To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively.
Adversarial Learning for Improved Onsets and Frames Music Transcription
Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance.
Robust sound event detection in bioacoustic sensor networks
As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise.
Neural Music Synthesis for Flexible Timbre Control
The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process --- creating audio samples from a score and instrument information --- is modeled using generative neural networks.
Adaptive pooling operators for weakly labeled sound event detection
In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality.
CREPE: A Convolutional Representation for Pitch Estimation
To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics.
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation.
Ranked #6 on
Environmental Sound Classification
on UrbanSound8K
(using extra training data)
