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. Paired with SoundFingerprinting.Emy it can be used to generate fingerprints from video content as well.
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 fingerprints 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 async Task StoreForLaterRetrieval(string pathToAudioFile)
{
var track = new TrackInfo("GBBKS1200164", "Skyfall", "Adele");
// create fingerprints
var hashedFingerprints = await FingerprintCommandBuilder.Instance
.BuildFingerprintCommand()
.From(pathToAudioFile)
.UsingServices(audioService)
.Hash();
// store hashes in the database for later retrieval
modelService.Insert(track, hashedFingerprints);
}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 async Task<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 = await QueryCommandBuilder.Instance.BuildQueryCommand()
.From(queryAudioFile, secondsToAnalyze, startAtSecond)
.UsingServices(modelService, audioService)
.Query();
return queryResult.BestMatch.Track;
}The default storage, which comes bundled with soundfingerprinting NuGet package, is a plain in-memory storage, available via InMemoryModelService class. If you plan to use an external persistent storage for audio fingerprints Emy is the preferred choice. It is a specialized storage developed for audio fingerprints. Emy provides a community version which is free for non-commercial use. You can try it with docker:
docker run -d -v /persistent-dir:/app/data -p 3399:3399 -p 3340:3340 addictedcs/soundfingerprinting.emy:latest
Emy provides a backoffice interface which you can access on port :3340. In order to insert and query Emy server please install SoundFingerprinting.Emy NuGet package.
Install-Package SoundFingerprinting.Emy
The package will provide you with EmyModelService class, which can substitute default InMemoryModelService.
// connect to Emy on port 3399
var emyModelService = EmyModelService.NewInstance("localhost", 3399);
// query Emy database
var queryResult = await QueryCommandBuilder.Instance.BuildQueryCommand()
.From(queryAudioFile, secondsToAnalyze, startAtSecond)
.UsingServices(modelService, audioService)
.Query();
// register matches s.t. they appear in the dashboard
emyModelService.RegisterMatches(queryResult.ResultEntries);Registering matches is now possible with EmyModelService. The results will be displayed in the Emy dashboard.
Similarly, SoundFingerprinting.Emy provides FFmpegAudioService, which supports a wide variety of formats for both audio and video fingerprinting. More details about FFmpegAudioService can be found below.
If you plan to use Emy storage in a commercial project please contact [email protected] for details. Enterprise version is ~12.5x faster when number of tracks exceeds ~10K, supports clustering, replication and much more. By using Emy you will also support core SoundFingerprinting library and its ongoing development. More details can be found here.
Previous storages are now considered deprecate, as Emy is now considered the default choice for persistent storage.
- Solr non-relational storage soundfingerprinting.solr. MIT licensed, useful when the number of tracks does not exceed 5000 tracks [deprecated].
- MSSQL soundfingerprinrint.sql [deprecated]. MIT licensed.
Read Supported Audio Formats page for details about different audio services and how you can use them in various operating systems.
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 queryConfidence- 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.MatchedAt- returns timestamp showing at what time did the match occured. Usefull for realtime queries.
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.
Read Different Types of Coverage to understand how query coverage is calculated.
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.
- 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
Audio Fingerprinting. Video Fingerprinting.
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.
© Soundfingerprinting, 2010-2021, [email protected]