Search Results for author:
Found 7 papers, 4 papers with code
Learning Audio Concepts from Counterfactual Natural Language
Conventional audio classification relied on predefined classes, lacking the ability to learn from free-form text.
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).
Exploring modality-agnostic representations for music classification
We train music instrument classifiers that can take both images or sounds as input, and perform comparably to sound-only or image-only classifiers.
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.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus.
