Search Results for author: Manoj Plakal

Found 10 papers, 5 papers with code

Dataset balancing can hurt model performance

no code implementations30 Jun 2023 R. Channing Moore, Daniel P. W. Ellis, Eduardo Fonseca, Shawn Hershey, Aren Jansen, Manoj Plakal

We find, however, that while balancing improves performance on the public AudioSet evaluation data it simultaneously hurts performance on an unpublished evaluation set collected under the same conditions.

Self-Supervised Learning from Automatically Separated Sound Scenes

1 code implementation5 May 2021 Eduardo Fonseca, Aren Jansen, Daniel P. W. Ellis, Scott Wisdom, Marco Tagliasacchi, John R. Hershey, Manoj Plakal, Shawn Hershey, R. Channing Moore, Xavier Serra

Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings.

Contrastive Learning Self-Supervised Learning

Audio tagging with noisy labels and minimal supervision

2 code implementations7 Jun 2019 Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Serra

The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller set of manually-labeled data, under a large vocabulary setting of 80 everyday sound classes.

Audio Tagging Task 2

Learning Sound Event Classifiers from Web Audio with Noisy Labels

2 code implementations4 Jan 2019 Eduardo Fonseca, Manoj Plakal, Daniel P. W. Ellis, Frederic Font, Xavier Favory, Xavier Serra

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.

General Classification Sound Event Detection

General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline

3 code implementations26 Jul 2018 Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, Xavier Favory, Jordi Pons, Xavier Serra

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.

Audio Tagging Task 2

Unsupervised Learning of Semantic Audio Representations

no code implementations6 Nov 2017 Aren Jansen, Manoj Plakal, Ratheet Pandya, Daniel P. W. Ellis, Shawn Hershey, Jiayang Liu, R. Channing Moore, Rif A. Saurous

Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds.

Audio Classification General Classification +2

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