Scan a mono 16bit signed PCM wave file for sound level exceeding certain threshold

volscan.py

import os
import numpy as np
import argparse
import scipy.io.wavfile as w


def GetHMS(total_seconds:int):
    hr = np.floor(total_seconds/3600)
    q1 = total_seconds % 3600
    m = np.floor(q1/60)
    s = q1 % 60
    return int(hr), int(m), int(s)


if __name__ == "__main__":
    cli_parser = argparse.ArgumentParser(
        description="Search for sound level above certain level in a wav file")
    cli_parser.add_argument("folder", help="Folder containing mono uncompressed wav files")
    cli_parser.add_argument("--threshold", type=float, default=-0.025,
                            help="Sound threshold. Lower value gives less higher sensitivity")
    cli_parser.add_argument("--min_interval", type=int, default=60,
                            help="minimum interval between reporting points")
    program_options = vars(cli_parser.parse_args())

    if not os.path.exists(program_options["folder"]):
        print("Folder not found")
        exit(1)
    os.chdir(program_options["folder"])
    arr_wav = [f for f in os.listdir() if f.endswith(".wav")]

    for f in arr_wav:
        rate, data = w.read(f, mmap=True)
        dn = np.abs((data - 32768)/32768)
        clean = np.nan_to_num(dn)
        total_time = np.floor(data.shape[0]/rate)
        db_array = []

        for x in np.linspace(0, total_time, total_time+1):
            start_index = int(x * rate)
            end_index = int(start_index + rate)
            data_subset = clean[start_index:end_index]
            if np.isnan(data_subset.shape[0]):
                continue
            sq = data_subset ** 2
            sum_of_square = np.sum(sq)
            rms = np.sqrt(sum_of_square/sq.shape[0])
            db = 20 * np.log10(rms)
            db_array.append(db)

        noisy_point = []
        np_db = np.array(db_array)
        for i, v in enumerate(np_db):
            if v > program_options["threshold"]:
                if len(noisy_point) == 0:
                    noisy_point.append(i + 1)
                if i - noisy_point[-1] > program_options["min_interval"]:
                    noisy_point.append(i+1)
        log_content = "Auto noise detection\n"
        for p in noisy_point:
            h, m, s = GetHMS(p)
            log_content += "{:0>2}:{:0>2}:{:0>2}\n".format(h, m, s)
            print("{:0>2}:{:0>2}:{:0>2}".format(h, m, s))
        out_file_name = f + ".txt"
        with open(out_file_name, "w") as outfile:
            outfile.write(log_content)