//! Temporal feature extraction module. //! //! Contains functions to extract & summarize the temporal aspects //! of a given Song. use crate::utils::Normalize; use crate::{BlissError, BlissResult}; use bliss_audio_aubio_rs::{OnsetMode, Tempo}; use log::warn; use ndarray::arr1; use ndarray_stats::interpolate::Midpoint; use ndarray_stats::Quantile1dExt; use noisy_float::prelude::*; /** * Beats per minutes ([BPM](https://en.wikipedia.org/wiki/Tempo#Measurement)) * detection object. * * It indicates the (subjective) "speed" of a music piece. The higher the BPM, * the "quicker" the song will feel. * * It uses `SpecFlux`, a phase-deviation onset detection function to perform * onset detection; it proved to be the best for finding out the BPM of a panel * of songs I had, but it could very well be replaced by something better in the * future. * * Ranges from 0 (theoretically...) to 206 BPM. (Even though aubio apparently * has trouble to identify tempo > 190 BPM - did not investigate too much) * */ pub(crate) struct BPMDesc { aubio_obj: Tempo, bpms: Vec, } // TODO>1.0 use the confidence value to discard this descriptor if confidence // is too low. impl BPMDesc { pub const WINDOW_SIZE: usize = 512; pub const HOP_SIZE: usize = BPMDesc::WINDOW_SIZE / 2; pub fn new(sample_rate: u32) -> BlissResult { Ok(BPMDesc { aubio_obj: Tempo::new( OnsetMode::SpecFlux, BPMDesc::WINDOW_SIZE, BPMDesc::HOP_SIZE, sample_rate, ) .map_err(|e| { BlissError::AnalysisError(format!("error while loading aubio tempo object: {}", e)) })?, bpms: Vec::new(), }) } pub fn do_(&mut self, chunk: &[f32]) -> BlissResult<()> { let result = self.aubio_obj.do_result(chunk).map_err(|e| { BlissError::AnalysisError(format!("aubio error while computing tempo {}", e)) })?; if result > 0. { self.bpms.push(self.aubio_obj.get_bpm()); } Ok(()) } /** * Compute score related to tempo. * Right now, basically returns the song's BPM. * * - `song` Song to compute score from */ pub fn get_value(&mut self) -> f32 { if self.bpms.is_empty() { warn!("Set tempo value to zero because no beats were found."); return -1.; } let median = arr1(&self.bpms) .mapv(n32) .quantile_mut(n64(0.5), &Midpoint) .unwrap(); self.normalize(median.into()) } } impl Normalize for BPMDesc { // See aubio/src/tempo/beattracking.c:387 // Should really be 413, needs testing const MAX_VALUE: f32 = 206.; const MIN_VALUE: f32 = 0.; } #[cfg(test)] mod tests { use super::*; use crate::{Song, SAMPLE_RATE}; use std::path::Path; #[test] fn test_tempo_real() { let song = Song::decode(Path::new("data/s16_mono_22_5kHz.flac")).unwrap(); let mut tempo_desc = BPMDesc::new(SAMPLE_RATE).unwrap(); for chunk in song.sample_array.chunks_exact(BPMDesc::HOP_SIZE) { tempo_desc.do_(&chunk).unwrap(); } assert!(0.01 > (0.378605 - tempo_desc.get_value()).abs()); } #[test] fn test_tempo_artificial() { let mut tempo_desc = BPMDesc::new(22050).unwrap(); // This gives one beat every second, so 60 BPM let mut one_chunk = vec![0.; 22000]; one_chunk.append(&mut vec![1.; 100]); let chunks = std::iter::repeat(one_chunk.iter()) .take(100) .flatten() .cloned() .collect::>(); for chunk in chunks.chunks_exact(BPMDesc::HOP_SIZE) { tempo_desc.do_(&chunk).unwrap(); } // -0.41 is 60 BPM normalized assert!(0.01 > (-0.416853 - tempo_desc.get_value()).abs()); } #[test] fn test_tempo_boundaries() { let mut tempo_desc = BPMDesc::new(10).unwrap(); let silence_chunk = vec![0.; 1024]; tempo_desc.do_(&silence_chunk).unwrap(); assert_eq!(-1., tempo_desc.get_value()); let mut tempo_desc = BPMDesc::new(22050).unwrap(); // The highest value I could obtain was with these params, even though // apparently the higher bound is 206 BPM, but here I found ~189 BPM. let mut one_chunk = vec![0.; 6989]; one_chunk.append(&mut vec![1.; 20]); let chunks = std::iter::repeat(one_chunk.iter()) .take(500) .flatten() .cloned() .collect::>(); for chunk in chunks.chunks_exact(BPMDesc::HOP_SIZE) { tempo_desc.do_(&chunk).unwrap(); } // 0.86 is 192BPM normalized assert!(0.01 > (0.86 - tempo_desc.get_value()).abs()); } }