There are some instances of the same bird in multiple locations (great horned owl). Presumably multiple recordings of the same bird. My initial reaction to them not being neighboring is to wonder about the quality of the result. Maybe better feature engineering needed to make this biologically relevant. Any other interpretations?
Thousands of bird sounds visualized using Google machine learning
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Re: Thousands of bird sounds visualized using Google machine learning
#32Unfortunately they only hint at the envisioned application in the video and don't provide any further links, but the idea is amazing: Use sounds to monitor bio-diversity. Imagine we'd not need cameras and lots of luck to "catch" proof of an animals existence but a grid of interconnected omnidirectional microphones. We'd get real time tracking of individual animals in 3D and could have smartphones literally point the…
Somewhat related, but there was a very good talk at MLConf 2017 about using sound to catch illegal logging in the Amazon. Similar premise: collect the sound, analyze the patterns, and classify.
likewise microphones in cities to detect gunshots
Re: Thousands of bird sounds visualized using Google machine learning
#33What task is made easier by this visualization?
Re: Thousands of bird sounds visualized using Google machine learning
#34Someone should make a Shazam/Soundhound app for bird calls, I'd definitely buy it if it could narrow it down to a subset of possibilities.
Re: Thousands of bird sounds visualized using Google machine learning
#35I poked around and also looked at a similar experiment, the Infinite Drum Machine: https://aiexperiments.withgoogle.com/drum-machine
Does anyone know what they are doing t-SNE on? i.e. are they just doing t-SNE on the raw waveforms? Or the MFCC spectrogram? Or what?
Re: Thousands of bird sounds visualized using Google machine learning
#36It's interesting, at the very local level I think most humans wouldn't think two adjacent bird sounds are all that similar. But if you drag along a long line and listen to a series of different birds you can "hear" a definite organized progression that seems to be organizing rhythm and major tones into groups.
Re: Thousands of bird sounds visualized using Google machine learning
#37Someone should make a Shazam/Soundhound app for bird calls, I'd definitely buy it if it could narrow it down to a subset of possibilities.
Been thinking of this but the library of birds sounds is private to Cornell if I'm not mistaken. The app , at least, should free and open source.
Re: Thousands of bird sounds visualized using Google machine learning
#38Unfortunately they only hint at the envisioned application in the video and don't provide any further links, but the idea is amazing: Use sounds to monitor bio-diversity. Imagine we'd not need cameras and lots of luck to "catch" proof of an animals existence but a grid of interconnected omnidirectional microphones. We'd get real time tracking of individual animals in 3D and could have smartphones literally point the…
I worked on this as part of a project with US Fish and Wildlife in 2015 for bats! We were monitoring local populations to track the spread of White Nose syndrome. It wasn't in 3D, but we linked the audio data / classifications with lat/long points and plotted population estimates on maps.
Re: Thousands of bird sounds visualized using Google machine learning
#39As a birder, this looks like a failed experiment to me. Or I don't understand what their goal was. The groupings make little sense in terms of what these species sound like. I'm guessing that's an artifact of the way they sampled the sounds, losing macro properties. Kind of like grouping the words 'paramour', 'enmity' and 'hamster' together bc they all contain /m/ sound.
Another birder here, totally agreed. The audio samples are all really short and downsampled.
Re: Thousands of bird sounds visualized using Google machine learning
#40This is my cats' favorite machine learning application so far!
Confusing my cat.