Onset Detection
11 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Metrical-accent Aware Vocal Onset Detection in Polyphonic Audio
The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings.
Towards an efficient deep learning model for musical onset detection
We first review the state-of-the-art deep learning models for MOD, and identify their shortcomings and challenges: (i) the lack of hyper-parameter tuning details, (ii) the non-availability of code for training models on other datasets, and (iii) ignoring the network capability when comparing different architectures.
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection can significantly benefit patients.
Onset detection: A new approach to QBH system
Query by Humming (QBH) is a system to provide a user with the song(s) which the user hums to the system.
An algorithm for onset detection of linguistic segments in continuous electroencephalogram signals
In order to build a fully asynchronous Brain Computer Interface based on imagined words in electroencephalogram signals as source, we need to solve the problem of detecting the onset of the imagined words.
Sleep syndromes onset detection based on automatic sleep staging algorithm
In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules.
Scorpiano -- A System for Automatic Music Transcription for Monophonic Piano Music
The main motivation for automatic music transcription is to make it possible for anyone playing a musical instrument, to be able to generate the music notes for a piece of music quickly and accurately.
The Efficacy of Self-Supervised Speech Models for Audio Representations
Extensive experiments on speech and non-speech audio datasets are conducted to investigate the representation abilities of our ensemble method and its single constituent model.
Annotation of Soft Onsets in String Ensemble Recordings
The problem is further exacerbated by a paucity of data containing expert annotations and research related to best practices for curating soft onset annotations for string instruments.
Towards trustworthy seizure onset detection using workflow notes
We find that our multilabel model significantly improves overall seizure onset detection performance (+5. 9 AUROC points) while greatly improving performance among subgroups (up to +8. 3 AUROC points), and decreases false positives on non-epileptiform abnormalities by 8 FPR points.