It's a very simple, if somewhat compute intensive technique which can be used for solving various problems, including measuring the time difference (aka lag) between two similar signals (the signals do not need to be identical).

Binaural: Inherits from Sound and provides methods for generating and manipulating binaural sounds, including advanced interaural time and intensity manipulation. Binaural sounds have left and a right channel properties.

Jan 5, 2017 · A popular approach: timeshift is the lag corresponding to the maximum cross-correlation coefficient. Here is how it works with an example: n = len(y1) corr = signal.correlate(y2, y1, mode='same') / np.sqrt(signal.correlate(y1, y1, mode='same')[int(n/2)] * signal.correlate(y2, y2, mode='same')[int(n/2)])

Works but seems completely equivalent to scipy.signal.correlate() from Gus answer which by default uses scipy.signal.fftconvolve as soon as its faster (i.e. as soon as quadratic times hurt which is soon).

Sep 12, 2021 · How is it possible to calculate the interaural time delay between two signals recorded underwater (ship sound signals)? Will linear prediction weight method be used for this?

Aug 14, 2020 · To estimate the time delay between two signals you can use the cross-correlation (np.correlate) between them and find the argmax of the cross-correlation function. τdelay = argmax ((f ∗ g)(t)), τ delay = argmax ((f ∗ g) (t)), this will estimate the time offset where the signals are best aligned.

Software for calculation of Interaural Level Differences (ILDs) in the horizontal plane via a set of parametric equations derived by fitting equations to the "self-consistent" smooth curves tabulated by Shaw and Vaillancourt (1985).

Learn how to calculate the Time Difference of Arrival (TDOA) in Python using the SciPy library. TDOA is a technique used in signal processing to estimate the location of a source based on the time delay of arrival of the signal at different receivers.

This model package provides NEURON codes associated with paper: Vasilkov V. A. and Tikidji-Hamburyan R. A.(2012) Accurate Detection of Interaural Time Differences by a Population of Slowly Integrating Neurons.

Cross-correlation of a signal with its time-delayed self. >>> import numpy as np >>> from scipy import signal >>> rng = np . random . default_rng () >>> x = rng . standard_normal ( 1000 ) >>> y = np . concatenate ([ rng . standard_normal ( 100 ), x ]) >>> correlation = signal . correlate ( x , y , mode = "full" ) >>> lags = signal . correlation ...