cqt-rs (Constant-Q Transform)

This library provides an implementation of the Constant-Q Transform (CQT) for time-frequency analysis of audio signals. The CQT is a method for analyzing a signal in the time-frequency domain, where the frequency resolution is logarithmic. This library provides the necessary functions to compute a CQT filterbank and apply it to an input audio signal.

Features

• Customizable CQT parameters, including:
  • Minimum and maximum frequencies
  • Number of bins
  • Sampling rate
  • Window length
• Custom error handling for invalid parameters and FFT computation errors
• Parallelization for performance optimization

Usage

1. Import the library:

toml [dependencies] cqt-rs = "0.1.0"

rust use cqt_rs::{CQTParams, Cqt};

1. Create a CQTParams instance with the desired parameters:

rust let cqt_params = CQTParams::new( 30.0, // Min frequency 4000.0, // Max frequency 12, // Number of bins 44000.0, // Sampling rate 4096.0 // Window length ).expect("Error creating CQTParams");

1. Create a Cqt instance using the CQTParams:

rust let cqt = Cqt::new(cqt_params);

1. Process an input audio signal using the Cqt instance:

rust let input_signal: Vec<f32> = vec![...]; // Your input audio signal let hop_size = 512; let cqt_features = cqt.process(&input_signal, hop_size).expect("Error computing CQT features");

Error Handling

The functions CQTParams::new and Cqt::process return Result types, which can be used to handle errors related to invalid parameters, FFT computation errors, or other issues:

rust match CQTParams::new(...) { Ok(cqt_params) => { // Use the CQTParams }, Err(error) => { println!("Error creating CQTParams: {:?}", error); // or handle the error in your way }, }

rust match cqt.process(&input_signal, hop_size) { Ok(cqt_features) => { // Use the CQT features }, Err(error) => { println!("Error computing CQT features: {:?}", error); // or handle the error in your way }, }

Dependencies

This library uses the following crates: - ndarray for efficient array computations - rustfft for FFT computation - hann-rs crate for Hann window computation

Performance

This implementation of the CQT is optimized for performance using parallelization with the rayon crate. The performance will depend on the input signal size, the chosen parameters, and your hardware.

Benchmarks of APPROXIMATE results

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| getcalculatedbasefreqratio | 14 BO | 11.001 (ns) | 11.059 (ns) | 11.140 (ns) | getcalculatedbasefreqratio (Cached) | 12 BO | 11.080 (ns) | 11.104 (ns) | 11.131 (ns) |

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| getcalculatedphasefactors | 2000 WL, 22000 SR | 2.2373 (µs) | 2.2450 ns (µs) | 2.2562 (µs) | getcalculatedphasefactors | 4000 WL, 44000 SR | 4.4032 (µs) | 4.4288 (µs) | 4.4652 (µs) | getcalculatedphasefactors (Cached) | 2048 WL, 22050 SR | 185.29 (ns) | 186.16 (ns) | 187.03 (ns) | getcalculatedphasefactors (Cached) | 4096 WL, 44100 SR | 381.80 (ns) | 385.20 (ns) | 389.52 (ns) |

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| calculatenorm | 2000 HW | 612.50 (ns) | 614.34 (ns) | 616.49 (ns) | calculatenorm | 4000 HW | 1.1675 (µs)| 1.1743 (µs) | 1.1857 (µs) | calculate_norm (Cached) | 4096 HW | 12.245 (ns) | 12.298 (ns) | 12.356 (ns) |

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| getcalculatedqfactor | 14 BO | 10.727 (ns) | 10.752 (ns) | 10.782 (ns) | getcalculatedqfactor (Cached) | 12 BO | 12.054 (ns) | 12.104 (ns) | 12.168 (ns) |

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| createcomplexhannwindow | 22000 SR, 2000 WL | 35.336 (µs) | 35.913 (µs) | 36.579 (µs) | createcomplexhannwindow | 44000 SR, 4000 WL | 43.626 (µs) | 44.265 (µs) | 44.999 (µs) | createcomplexhannwindow (Cached) | 22050 SR, 2048 WL | 35.964 (µs) | 36.381 (µs) | 36.845 (µs) | createcomplexhannwindow (Cached) | 44100 SR, 4096 WL | 43.111 (µs) | 43.552 (µs) | 44.017 (µs) |

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| computecqtfilterbank | 22000 SR, 2000 WL | 1.3798 (ms) | 1.4057 (ms) | 1.4354 (ms) | computecqtfilterbank | 44000 SR, 4000 WL | 2.5221 (ms) | 2.5737 (ms) | 2.6335 (ms) | computecqtfilterbank (Cached) | 22050 SR, 2048 WL | 1.3117 (ms) | 1.3387 (ms) | 1.3686 (ms) | computecqtfilterbank (Cached) | 44100 SR, 4096 WL | 2.6103 (ms) | 2.6785 (ms) | 2.7535 (ms) |

|Metric | Size | Minimum Time | Average Time | Maximum Time | :-------:|-----:|------------------:|------------------:|------------------:| cqt.process | 22000 SR, 2000 WL | 12.798 (ms) | 13.037 (ms) | 13.308 (ms) | cqt.process | 44000 SR, 4000 WL | 47.653 (ms) | 48.368 (ms) | 49.191 (ms) | cqt.process (Cached) | 22050 SR, 2048 WL | 12.934 (ms) | 13.133 (ms) | 13.350 (ms) | cqt.process (Cached) | 44100 SR, 4096 WL | 45.214 (ms) | 45.842 (ms) | 46.548 (ms) |