Perceptual similarity between two audio sequences

I would like to get some sort of distance measure between two pieces of audio. For example, I want to compare the sound of an animal to the sound of a human mimicking that animal, and then return a score of how similar the sounds were.

It seems like a difficult problem. What would be the best way to approach it? I was thinking to extract a couple of features from the audio signals and then do a Euclidian distance or cosine similarity (or something like that) on those features. What kind of features would be easy to extract and useful to determine the perceptual difference between sounds?

(I saw somewhere that Shazam uses hashing, but that's a different problem because there the two pieces of audio being compared are fundamentally 开发者_如何学JAVAthe same, but one has more noise. Here, the two pieces of audio are not the same, they are just perceptually similar.)

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The process for comparing a set of sounds for similarities is called Content Based Audio Indexing, Retrieval, and Fingerprinting in computer science research.

One method of doing this is to:

1. Run several bits of signal processing on each audio file to extract features, such as pitch over time, frequency spectrum, autocorrelation, dynamic range, transients, etc.

2. Put all the features for each audio file into a multi-dimensional array and dump each multi-dimensional array into a database

3. Use optimization techniques (such as gradient descent) to find the best match for a given audio file in your database of multi-dimensional data.

The trick to making this work well is which features to pick. Doing this automatically and getting good results can be tricky. The guys at Pandora do this really well, and in my opinion they have the best similarity matching around. They encode their vectors by hand though, by having people listen to music and rate them in many different ways. See their Music Genome Project and List of Music Genome Project attributes for more info.

For automatic distance measurements, there are several projects that do stuff like this, including marsysas, MusicBrainz, and EchoNest.

Echonest has one of the simplest APIs I've seen in this space. Very easy to get started.

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I'd suggest looking into spectrum analysis. Whilst this isn't as straightforward as you're most likely wanting, I'd expect that decomposing the audio into it's underlying frequencies would provide some very useful data to analyse. Check out this link

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Your first step will definitely be taking a Fourier Transform(FT) of the sound waves. If you perform an FT on the data with respect to Frequency over Time¹, you'll be able to compare how often certain key frequencies are hit over the course of the noise.

Perhaps you could also subtract one wave from the other, to get a sort of stepwise difference function. Assuming the mock-noise follows the same frequency and pitch trends² as the original noise, you could calculate the line of best fit to the points of the difference function. Comparing the best fit line against a line of best fit taken of the original sound wave, you could average out a trend line to use as the basis of comparison. Granted, this would be a very loose comparison method.

_{- 1. hz/ms, perhaps? I'm not familiar with the unit magnitude being worked with here, I generally work in the femto- to nano- range.}

^{- 2. So long as ∀ΔT, Δ_Pitch/ΔT & Δ_Frequency/ΔT are within some tolerance x.}

_{- Edited for formatting, and because I actually forgot to finish writing the full answer.}