Towards a Universal Method for Meaningful Signal Detection
This addresses the problem of automated signal analysis for researchers in fields like linguistics and bioacoustics, but it is incremental as it builds on existing clustering and description length techniques.
The paper tackles the problem of detecting meaningful signals without deciphering content by proposing a method that analyzes waveforms to output a meaningfulness score, showing it gives high scores to human speech, moderate scores to animal vocalizations, and low scores to ambient noise.
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.