NoveltyCurve
standard mode | Rhythm category
Inputs
- frequencyBands (vector_vector_real) - the frequency bands
Outputs
- novelty (vector_real) - the novelty curve as a single vector
Parameters
- frameRate (real ∈ [1, ∞), default = 344.531) :
- the sampling rate of the input audio
- normalize (bool ∈ {true, false}, default = false) :
- whether to normalize each band's energy
- weightCurve (vector_real, default = []) :
- vector containing the weights for each frequency band. Only if weightCurveType==supplied
- weightCurveType (string ∈ {flat, triangle, inverse_triangle, parabola, inverse_parabola, linear, quadratic, inverse_quadratic, hybrid, supplied}, default = hybrid) :
- the type of weighting to be used for the bands novelty
Description
This algorithm computes the "novelty curve" (Grosche & Müller, 2009) onset detection function. The algorithm expects as an input a frame-wise sequence of frequency-bands energies or spectrum magnitudes as originally proposed in [1] (see FrequencyBands and Spectrum algorithms). Novelty in each band (or frequency bin) is computed as a derivative between log-compressed energy (magnitude) values in consequent frames. The overall novelty value is then computed as a weighted sum that can be configured using 'weightCurve' parameter. The resulting novelty curve can be used for beat tracking and onset detection (see BpmHistogram and Onsets).
Notes:
- Recommended frame/hop size for spectrum computation is 2048/1024 samples (44.1 kHz sampling rate) [2].
- Log compression is applied with C=1000 as in [1].
- Frequency bands energies (see FrequencyBands) as well as bin magnitudes for the whole spectrum can be used as an input. The implementation for the original algorithm [2] works with spectrum bin magnitudes for which novelty functions are computed separately and are then summarized into bands.
- In the case if 'weightCurve' is set to 'hybrid' a complex combination of flat, quadratic, linear and inverse quadratic weight curves is used. It was reported to improve performance of beat tracking in some informal in-house experiments (Note: this information is probably outdated).
References:
- Grosche, P. & Müller, M. (2009). A mid-level representation for capturing dominant tempo and pulse information in music recordings. International Society for Music Information Retrieval Conference (ISMIR 2009).
- Tempogram Toolbox (Matlab implementation), http://resources.mpi%2Dinf.mpg.de/MIR/tempogramtoolbox
See also
BpmHistogram (standard) BpmHistogram (streaming) FrequencyBands (standard) FrequencyBands (streaming) Histogram (standard) Histogram (streaming) NoveltyCurve (streaming) Onsets (standard) Onsets (streaming) Spectrum (standard) Spectrum (streaming)