Towards a Universal Method for Meaningful Signal Detection
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Abstract (arXiv)
It is known that human speech and certain animal vocalizations can convey meaningful content because we can decipher the content that a given utterance does convey. This paper explores an alternative approach to determining whether a signal is meaningful, one that analyzes only the signal itself and is independent of what the conveyed meaning might be. We devise a method that takes a waveform as input and outputs a score indicating its degree of `meaningfulness`. We cluster contiguous portions of the input to minimize the total description length, and then take the length of the code of the assigned cluster labels as meaningfulness score. We evaluate our method empirically, against several baselines, and show that it is the only one to give a high score to human speech in various languages and with various speakers, a moderate score to animal vocalizations from birds and orcas, and a low score to ambient noise from various sources.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 28 Jul 2024.
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7 of 31 references.
References · 7
- 2020Wavesplit: End-to-End Speech Separation by Speaker Clusteringcited by 74
- 2024A Modular Approach for Multimodal Summarization of TV Showscited by 13
- 2023Minimum Description Length Clustering to Measure Meaningful Image Complexitycited by 10
- 2023Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentationcited by 7
- 2022Efficient Deep Clustering of Human Activities and How to Improve Evaluationcited by 5
- 2017Measures of Entropy and Complexity in altered states of consciousnesscited by 1
- 2023Do Orcas Have Semantic Language? Machine Learning to Predict Orca Behaviors Using Partially Labeled Vocalization Datacited by 1
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Citing papers per year: 2025: 2.
2 papers.