Minimal Scala library to process audio files. Only WAVE files are supported now.

This library allows to:

• Wrap an audio file and extract metadata as well as audio data
• Compute a normalized waveform
• Detect audio tempo in beats per minute

The library uses JWave and adds it as a git submodule in lib/jwave. To have a working copy of the library, after cloning the repository you need to init and update the submodules.

git submodule init
git submodule update

The scala audio library can be included into you sbt project as:

resolvers += Resolver.bintrayRepo("mziccard", "maven")
libraryDependencies ++= Seq("me.mziccard" %% "scala-audio-file" % "0.2")

All releases are pushed to the maven repository. Latest release is:

• scala-audio-file v0.2 compatible with Scala 2.10 and Scala 2.11

A WAVE file can be opened via the WavFile class. The class has private constructor and cannot be directly istantiated, use the companion object instead. WavFile provides several functionalites to access audio data and metadata.
Audio samples can be read as floating point values in the interval [0,1].

val audioFile = WavFile("filename.wav");
val readBuffer = new Array[Double](1024*audioFile.numChannels);
val framesRead = audioFile.readNormalizedFrames(readBuffer, 1024);

or as integer values.

val audioFile = WavFile("filename.wav");
val readBuffer = new Array[Double](1024*audioFile.numChannels);
val framesRead = audioFile.readFrames(readBuffer, 1024);

A waveform can be computed for the audio track by wrapping the file object inside a Waveform object. A waveform is represented as a Scala array of Double values in the interval [0,1]. User can specify the amount of points per minutes the waveform should be made of.

val audioFile = WavFile("filename.wav");
val waveform = Waveform(audioFile);
var waveformJSON = Waveform.formatToJson(waveform.getWaveform(512), 2);

Waveform companion object provides a method to export the waveform as a JSON array with a controlled amount of decimal digits.

Three classes are available to compute audio file tempo in bpm. Both classes implement the BPMDetector trait.

trait BPMDetector {
  def bpm() : Double;
}

The SoundEnergyBPMDetector applies a simple bpm detection algorithm based on identificaiton of energy peaks in the track's audio data. No transform is applied to data, the class implementes the algorithm #3 described here.

val audioFile = WavFile("filename.wav");
val tempo = SoundEnergyBPMDetector(audioFile).bpm;

The FilterBPMDetector applies a more complex algorithm based on filters. Data are filtered, for instance low-passed, before detecting peaks. Tempo is computed as the most recurring distance across identified peaks. The BiquadFilter class, implementing a biquad filter, can be used in combination with FilterBPMDetector.

val audioFile = WavFile("filename.wav");
val filter = BiquadFilter (
  audioFile.sampleRate,
  audioFile.numChannels,
  FilterType.LowPass)
val detector = FilterBPMDetector(audioFile, filter);
val tempo = detector.bpm

More complex factory methods are also available in FilterBPMDetector that allow more fine-grained configuration. For more details on the algorithm implemented by FilterBPMDetector you can have a look at Beatport's blog.

The class WaveletBPMDetector applies a more precise algorithm (described in this paper) based on the Discrete Wavelet Transform (DWT). The algorithm operates on windows of frames so the class can be istantiated by providing an audio file, the size of a window in number of frames and the type of wavelet.

val audioFile = WavFile("filename.wav")
val tempo = WaveletBPMDetector(
              audioFile, 
              131072, 
              WaveletBPMDetector.Daubechies4).bpm

So far only Haar and Daubechies4 wavelets are supported. Due to the way DWT is implemented the size of a window must be a power of 2. An additional integer parameter can be given to the factory method providing the maximum number of windows to process. If not specified the entire track is processed.