soundfingerprinting is a C# framework designed for companies, enthusiasts, researchers in the fields of audio and digital signal processing, data mining and audio recognition. It implements an efficient algorithm which provides fast insert and retrieval of acoustic fingerprints with high precision and recall rate.
Below code snippet shows how to extract acoustic fingerprints from an audio file and later use them as identifiers to recognize unknown audio query. These sub-fingerprints (or fingerprints, two terms used interchangeably) will be stored in a configurable datastore.
private readonly IModelService modelService = new InMemoryModelService(); // store fingerprints in RAM
private readonly IAudioService audioService = new SoundFingerprintingAudioService(); // default audio library
public void StoreAudioFileFingerprintsInStorageForLaterRetrieval(string pathToAudioFile)
{
var track = new TrackInfo("GBBKS1200164", "Skyfall", "Adele", 290d);
// create fingerprints
var hashedFingerprints = FingerprintCommandBuilder.Instance
.BuildFingerprintCommand()
.From(pathToAudioFile)
.UsingServices(audioService)
.Hash()
.Result;
// store hashes in the database for later retrieval
modelService.Insert(track, hashedFingerprints);
}There are four storages available for use. The default storage, which comes bundled with soundfingerprinting NuGet package, is a plain in-memory storage, available via InMemoryModelService. Other storages that you can use are:
- SoundFingerprinting.Emy contact me at [email protected] for early access to a enterprise fingerprinting storage that is both super fast and resilient.
- Solr efficient non-relational storage soundfingerprinting.solr. MIT licensed, useful when the number of tracks does not exceed 1000 tracks.
- MSSQL soundfingerprinrint.sql [deprecated]. MIT licensed, still used, but not supported anymore due to it's inefficiency.
- Starting with v3.2.0
InMemoryModelServicecan be serialized to filesystem, and reloaded on application startup. Useful for scenarious when you don't want to introduce external data storages.
Once you've inserted the fingerprints into the datastore, later you might want to query the storage in order to recognize the song those samples you have. The origin of query samples may vary: file, URL, microphone, radio tuner, etc. It's up to your application, where you get the samples from.
public TrackData GetBestMatchForSong(string queryAudioFile)
{
int secondsToAnalyze = 10; // number of seconds to analyze from query file
int startAtSecond = 0; // start at the begining
// query the underlying database for similar audio sub-fingerprints
var queryResult = QueryCommandBuilder.Instance.BuildQueryCommand()
.From(queryAudioFile, secondsToAnalyze, startAtSecond)
.UsingServices(modelService, audioService)
.Query()
.Result;
return queryResult.BestMatch.Track;
}Every ResultEntry object will contain the following information:
Track- matched track from the datastoreQueryMatchLength- returns how many query seconds matched the resulting trackQueryMatchStartsAt- returns time position where resulting track started to match in the queryTrackMatchStartsAt- returns time position where the query started to match in the resulting trackTrackStartsAt- returns an approximation where does the matched track starts, always relative to the queryCoverage- returns a value between [0, 1], informing how much the query covered the resulting track (i.e. a 2 minutes query found a 30 seconds track within it, starting at 100th second, coverage will be equal to (120 - 100)/30 ~= 0.66)Confidence- returns a value between [0, 1]. A value below 0.15 is most probably a false positive. A value bigger than 0.15 is very likely to be an exact match. For good audio quality queries you can expect getting a confidence > 0.5.
Stats contains useful statistics information for fine-tuning the algorithm:
QueryDuration- time in milliseconds spend just querying the fingerprints datasource.FingerprintingDuration- time in milliseconds spent generating the acousting fingerprints from the media file.TotalTracksAnalyzed- total # of tracks analyzed during query time. If this number exceeds 50, try optimizing your configuration.TotalFingerprintsAnalyzed- total # of fingerprints analyzed during query time. If this number exceeds 500, try optimizing your configuration.
Version 6.0.0 provides a slightly improved IModelService interface. Now you can insert TrackInfo and it's corresponding fingerprints in one method call. The signatures of the fingerprints stayed the same, no need to re-index your tracks. Also, instead of inserting TrackData objects a new lightweight data class has been added: TrackInfo.
Version 5.2.0 provides a query configuration option AllowMultipleMatchesOfTheSameTrackInQuery which will instruct the framework to consider the use case of having the same track matched multiple times within the same query. This is handy for long queries that can contain same match scattered across the query. Default value is false.
Starting from version 5.1.0 the fingerprints signature has changed to be more resilient to noise. You can try HighPrecisionFingerprintConfiguration in case your audio samples come from recordings that contain ambient noise. All users that migrate to 5.1.x have to re-index the data, since fingerprint signatures from <= 5.0.x version are not compatible.
Starting from version 5.0.0 soundfingerprinting library supports .NET Standard 2.0. You can run the application not only on Window environment but on any other .NET Standard compliant runtime.
Default SoundFingerprintingAudioService supports only wave file at the input. If you would like to process other formats, consider using below extensions:
- SoundFingerprinting.Audio.NAudio - replacement for default
SoundFingerprintingAudioServiceaudio service. Provides support for .mp3 audio processing. Runs only on Windows as it uses NAudio framework for underlying decoding and resampling. - SoundFingerprinting.Audio.Bass - Bass.Net audio library integration, comes as a replacement for default service. Works faster than the default or NAudio, more accurate resampling, supports multiple audio formats (.wav, .ogg, .mp3, .flac). Bass is free for non-comercial use. Recommended for enterprise users.
- All demo apps are now located in separate git repositories, duplicates detector, sound tools.
Fingerprinting and Querying algorithms can be easily parametrized with corresponding configuration objects passed as parameters on command creation.
var hashDatas = FingerprintCommandBuilder.Instance
.BuildFingerprintCommand()
.From(samples)
.WithFingerprintConfig(new HighPrecisionFingerprintConfiguration())
.UsingServices(audioService)
.Hash()
.Result;Similarly during query time you can specify a more high precision query configuration in case if you are trying to detect audio in noisy environments.
QueryResult queryResult = QueryCommandBuilder.Instances
.BuildQueryCommand()
.From(PathToFile)
.WithQueryConfig(new HighPrecisionQueryConfiguration())
.UsingServices(modelService, audioService)
.Query()
.Result;There are 3 pre-built configurations to choose from: LowLatency, Default, HighPrecision. Nevertheless you are not limited to use just these 3. You can ammed each particular configuration property by your own via overloads.
In case you need directions for fine-tunning the algorithm for your particular use case do not hesitate to contact me. Specifically if you are trying to use it on mobile platforms HighPrecisionFingerprintConfiguration may not be accurate enought.
Please use fingerprinting configuration counterpart during query (i.e. HighPrecisionFingerprintConfiguration with HighPrecisionQueryConfiguration). Different configuration analyze different spectrum ranges, thus they have to be used in pair.
Most critical parts of the soundfingerprinting framework are interchangeable with extensions. If you want to use NAudio as the underlying audio processing library just install SoundFingerprinting.Audio.NAudio package and substitute IAudioService with NAudioService. Same holds for database storages. Install the extensions which you want to use (i.e. SoundFingerprinting.Solr) and provide new ModelService where needed.
Links to the third party libraries used by soundfingerprinting project.
- Can I apply this algorithm for speech recognition purposes?
No. The granularity of one fingerprint is roughly ~1.46 seconds.
- Can the algorithm detect exact query position in resulted track?
Yes.
- Can I use SoundFingerprinting to detect ads in radio streams?
Yes. Actually this is the most frequent use-case where SoundFingerprinting was successfully used.
- Will SoundFingerprinting match tracks with samples captured in noisy environment?
Yes, try out
HighPrecisionconfigurations, or contact me for additional guidance.
- Can I use SoundFingerprinting framework on Mono or .NET Core app?
Yes. SoundFingerprinting can be used in cross-platform applications. Keep in mind though, cross platform audio service
SoundFingerprintingAudioServicesupports only .wav files at it's input.
- How many tracks can I store in
InMemoryModelService?
100 hours of content with
HighPrecisionfingerprinting configuration will yeild in ~5GB or RAM usage.
git clone [email protected]:AddictedCS/soundfingerprinting.git
In order to build latest version of the SoundFingerprinting assembly run the following command from repository root.
.\build.cmd
Install-Package SoundFingerprinting
soundfingerprinting employs computer vision techniques to generate audio fingerprints. The fingerprints are generated from spectrogram images taken every N samples. Below is a 30 seconds long non-overlaping spectrogram cut at 318-2000Hz frequency range.
After a list of subsequent transformations these are converted into hashes, which are stored and used at query time. The fingerprints are robust to degradations to a certain degree. The DefaultFingerprintConfiguration class can be successfully used for radio stream monitoring. It handles well different audio formats, aliased signals and sampling differences accross tracks. Ambient noise is a different beast and you will probably need HighPrecisionFingerprintConfiguration to deal with it.
My description of the algorithm alogside with the demo project can be found on CodeProject. The article is from 2011, and may be outdated. The demo project is a Audio File Duplicates Detector. Its latest source code can be found here. Its a WPF MVVM project that uses the algorithm to detect what files are perceptually very similar.
If you want to contribute you are welcome to open issues or discuss on issues page. Feel free to contact me for any remarks, ideas, bug reports etc.
The framework is provided under MIT license agreement.
Special thanks to JetBrains for providing this project with a license for ReSharper!
© Soundfingerprinting, 2010-2018, [email protected]