I have an audio recorded, recorded by a 2 mics array. I want to a Python code that get the audio as input and estimate the RT60 reverberation time. I do have a function that estimates RT60 from a impulse response (IR). See below.

In theory I could try to extract the IR from the recorded audio, but since it involves deconvolution and I don't necessarily have the broadcasted clean audio, this seems too complicated and not fisible.

Therefore, I ask if someone tried to tackle this problem and have a Python code that estimates the RT60 given a multichannel audio recording.

import numpy as np

# ==============================================================================
# Measure RT60
# ==============================================================================
def measure_rt60(h, fs=1, decay_db=60, plot=False, rt60_tgt=None):
    Analyze the RT60 of an impulse response. Optionaly plots some useful information.

    `h`: array_like
        The impulse response.
    `fs`: float or int, optional
        The sampling frequency of h (default to 1, i.e., samples).
    `decay_db`: float or int, optional
        The decay in decibels for which we actually estimate the time. Although
        we would like to estimate the RT60, it might not be practical. Instead,
        we measure the RT20 or RT30 and extrapolate to RT60.
    `plot`: bool, optional
        If set to ``True``, the power decay and different estimated values will
        be plotted (default False).
    `rt60_tgt`: float
        This parameter can be used to indicate a target RT60 to which we want
        to compare the estimated value.

    h = np.array(h)
    fs = float(fs)

    # The power of the impulse response in dB
    power = h ** 2
    energy = np.cumsum(power[::-1])[::-1]  # Integration according to Schroeder

    # remove the possibly all zero tail
    i_nz = np.max(np.where(energy > 0)[0])
    energy = energy[:i_nz]
    energy_db = 10 * np.log10(energy)
    energy_db -= energy_db[0]

    # -5 dB headroom
    i_5db = np.min(np.where(-5 - energy_db > 0)[0])
    e_5db = energy_db[i_5db]
    t_5db = i_5db / fs

    # after decay
    i_decay = np.min(np.where(-5 - decay_db - energy_db > 0)[0])
    t_decay = i_decay / fs

    # compute the decay time
    decay_time = t_decay - t_5db
    est_rt60 = (60 / decay_db) * decay_time

    if plot:
        import matplotlib.pyplot as plt

        # Remove clip power below to minimum energy (for plotting purpose mostly)
        energy_min = energy[-1]
        energy_db_min = energy_db[-1]
        power[power < energy[-1]] = energy_min
        power_db = 10 * np.log10(power)
        power_db -= np.max(power_db)

        # time vector
        def get_time(x, fs):
            return np.arange(x.shape[0]) / fs - i_5db / fs

        T = get_time(power_db, fs)

        # plot power and energy
        plt.plot(get_time(energy_db, fs), energy_db, label="Energy")

        # now the linear fit
        plt.plot([0, est_rt60], [e_5db, -65], "--", label="Linear Fit")
        plt.plot(T, np.ones_like(T) * -60, "--", label="-60 dB")
            est_rt60, energy_db_min, 0, linestyles="dashed", label="Estimated RT60"

        if rt60_tgt is not None:
            plt.vlines(rt60_tgt, energy_db_min, 0, label="Target RT60")



    return est_rt60
  • $\begingroup$ What was the audio stimuli used? Was it something that approximates an impulse sound, or something else? $\endgroup$
    – Jon Nordby
    Feb 5, 2023 at 17:31

1 Answer 1


Blind estimation of reverberation time has been actively researched in room acoustics for at least 20 years. An early paper is Blind estimation of reverberation time by Ratnam et.al (2003).

And a recent approach using microphone arrays is described in Deep Neural Network Based Blind Estimation of Reverberation Time Based on Multi-channel Microphones by Myungin Lee and Joon-Hyuk Chang (2018).


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