ReviewPatterns (New York, N.Y.)2022
Audio self-supervised learning: A survey.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- A frequency analysis of filterbank initialisation and noise augmentation for LEAF.Scientific reports · 2026Article
- A hybrid CNN and reinforcement learning framework for speaker identification using Mel-Spectrogram and continuous wavelet transform features.Scientific reports · 2026Article
- Reconstruction Modeling and Validation of Brown Croaker (Sensors (Basel, Switzerland) · 2025Article
- CLUMM: Contrastive Learning for Unobtrusive Motion Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Contrastive self-supervised learning for neurodegenerative disorder classification.Frontiers in neuroinformatics · 2025Article
- Exploring simple triplet representation learning.Computational and structural biotechnology journal · 2024Article
- SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics.Sensors (Basel, Switzerland) · 2024Article
- SonicGuard Sensor-A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study.Sensors (Basel, Switzerland) · 2024Article
- Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised learning (SSL) targets discovering general representations from large-scale data. This, through the use of pre-trained SSL models for downstream tasks, alleviates the need for human annotation, which is an expensive and time-consuming task. Its success in the fields of computer vision and natural language processing have prompted its recent adoption into the field of audio and speech processing. Comprehensive reviews summarizing the knowledge in audio SSL are currently missing. To fill this gap, we provide an overview of the SSL methods used for audio and speech processing applications. Herein, we also summarize the empirical works that exploit audio modality in multi-modal SSL frameworks and the existing suitable benchmarks to evaluate the power of SSL in the computer audition domain. Finally, we discuss some open problems and point out the future directions in the development of audio SSL.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.