stream-audio-fingerprint - npm Package Compare versions

Comparing version 1.0.0 to 1.0.1

package.json

		{
		"name": "stream-audio-fingerprint",
		"version": "1.0.0",
		"version": "1.0.1",
		"description": "Audio landmark fingerprinting as a Node Stream module",
		"main": "codegen_demo.js",
		"main": "codegen_landmark.js",
		"scripts": {
		@@ -19,3 +19,7 @@ "test": "echo \"Error: no test specified\" && exit 1"
		"node-png": "^0.4.3"
		}
		},
		"bugs": {
		"url": "https://github.com/dest4/stream-audio-fingerprint/issues"
		},
		"homepage": "https://github.com/dest4/stream-audio-fingerprint"
		}

README.md

		@@ -1,2 +0,69 @@
		# stream-audio-fingerprint
		Audio landmark fingerprinting as a Node Stream module
		# Audio landmark fingerprinting as a Node Stream module

		This module is a duplex stream (instance of stream.Transform) that converts a PCM audio signal into a series of audio fingerprints. It works with audio tracks as well as with unlimited audio streams, e.g. broadcast radio.

		## Credits

		The [acoustic fingerprinting](https://en.wikipedia.org/wiki/Acoustic_fingerprint) technique used here is the landmark algorithm, as described in the [Shazam 2003 paper](http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf).
		The implementation in ```codegen_landmark.js``` has been inspired by the MATLAB routine of D. Ellis ["Robust Landmark-Based Audio Fingerprinting" (2009)](http://labrosa.ee.columbia.edu/matlab/fingerprint/). One significant difference with Ellis' implementation is that this module can handle unlimited audio streams, e.g. radio, and not only finished audio tracks.

		Note the existence of another good landmark fingerprinter in Python, [dejavu](https://github.com/worldveil/dejavu).

		## Description

		In a nutshell,
		- a spectrogram is computed from the audio signal
		- significant peaks are chosen in this time-frequency map. a latency of 500ms is used to determine if a peak is not followed by a bigger peak.
		- fingerprints are computed by linking peaks with ```dt```, ```f1``` and ```f2```, ready to be inserted in a database or to be compared with other fingerprints.

		![Spectrogram, peaks and pairs](out-fft.png)

		In the background, about 12s of musical content is represented as a spectrogram (top frequency is about 5kHz). The blue marks are the chosen spectrogram peaks. Grey lines are peaks pairs that each lead to a fingerprint.

		![Threshold and peaks](out-thr.png)

		Given the same audio, this figure shows the same peaks and the internal forward threshold that prevent peaks from being too close in time and frequency. The backward threshold selection is not represented here.

		## Usage

		```sh
		npm install stream-audio-fingerprint
		```

		The algorithm is in ```codegen_landmark.js```.

		A demo usage is proposed in ```codegen_demo.js```.

		```javascript
		var decoder = require('child_process').spawn('ffmpeg', [
		'-i', 'pipe:0',
		'-acodec', 'pcm_s16le',
		'-ar', 11025,
		'-ac', 1,
		'-f', 'wav',
		'-v', 'fatal',
		'pipe:1'
		], { stdio: ['pipe', 'pipe', process.stderr] });
		process.stdin.pipe(decoder.stdin);

		var Codegen = require("stream-audio-fingerprint");
		var fingerprinter = new Codegen();
		decoder.stdout.pipe(fingerprinter);

		fingerprinter.on("data", function(data) {
		for (var i=0; i<data.tcodes.length; i++) {
		console.log("time=" + data.tcodes[i] + " fingerprint=" + data.hcodes[i]);
		}
		});
		```

		and then we pipe audio data, either a stream or a file

		```sh
		curl http://radiofg.impek.com/fg \| nodejs codegen_demo.js
		cat awesome_music.mp3 \| nodejs codegen_demo.js
		```

		## License

		See LICENSE file.

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