audiosegment.py
def human_audible(self):
        """
        Returns an estimate of whether this AudioSegment is mostly human audible or not.
        This is done by taking an FFT of the segment and checking if the SPL of the segment
        falls below the function `f(x) = 40.11453 - 0.01683697x + 1.406211e-6x^2 - 2.371512e-11x^3`,
        where x is the most characteristic frequency in the FFT.

        Note that this method is essentially trying to determine if the estimated SPL of the segment
        falls below the threshold of human hearing, which changes over frequency. If you graph the
        threshold over different frequencies, you get what is called an audiogram. The equation above
        is derived as a curve that tries to fit a typical audiogram, specifically as found in
        Hearing Thresholds by Yost and Killion, 1997 (see https://www.etymotic.com/media/publications/erl-0096-1997.pdf).

        Sources of error are: 1) The SPL of an AudioSegment is merely an approximation; 2) this curve
        is not a perfect fit, and besides, it is only an approximation of a typical audiogram; 3) the
        algorithm uses a characteristic frequency, which is only really going to be a thing for
        short segments or for segments which are dominated by a single frequency.

        :returns: `True` if we estimate that this sound is mostly human audible. `False` if we think
                  it is not.
        """
        hist_bins, hist_vals = self.fft()
        hist_vals_real_normed = np.abs(hist_vals) / len(hist_vals)
        f_characteristic = hist_bins[np.argmax(hist_vals_real_normed)]

        threshold_fc = 40.11453 - (0.01683697 * f_characteristic) + (1.406211e-6 * f_characteristic ** 2) - (2.371512e-11 * f_characteristic ** 3)

        return self.spl >= threshold_fc
def human_audible(self):
        """
        Returns an estimate of whether this AudioSegment is mostly human audible or not.
        This is done by taking an FFT of the segment and checking if the SPL of the segment
        falls below the function `f(x) = 40.11453 - 0.01683697x + 1.406211e-6x^2 - 2.371512e-11x^3`,
        where x is the most characteristic frequency in the FFT.

        Note that this method is essentially trying to determine if the estimated SPL of the segment
        falls below the threshold of human hearing, which changes over frequency. If you graph the
        threshold over different frequencies, you get what is called an audiogram. The equation above
        is derived as a curve that tries to fit a typical audiogram, specifically as found in
        Hearing Thresholds by Yost and Killion, 1997 (see https://www.etymotic.com/media/publications/erl-0096-1997.pdf).

        Sources of error are: 1) The SPL of an AudioSegment is merely an approximation; 2) this curve
        is not a perfect fit, and besides, it is only an approximation of a typical audiogram; 3) the
        algorithm uses a characteristic frequency, which is only really going to be a thing for
        short segments or for segments which are dominated by a single frequency.

        :returns: `True` if we estimate that this sound is mostly human audible. `False` if we think
                  it is not.
        """
        hist_bins, hist_vals = self.fft()
        hist_vals_real_normed = np.abs(hist_vals) / len(hist_vals)
        f_characteristic = hist_bins[np.argmax(hist_vals_real_normed)]

        threshold_fc = 40.11453 - (0.01683697 * f_characteristic) + (1.406211e-6 * f_characteristic ** 2) - (2.371512e-11 * f_characteristic ** 3)

        return self.spl >= threshold_fc
def human_audible ( self ) : hist_bins , hist_vals = self . fft ( ) hist_vals_real_normed = np . abs ( hist_vals ) / len ( hist_vals ) f_characteristic = hist_bins [ np . argmax ( hist_vals_real_normed ) ] threshold_fc = 40.11453 - ( 0.01683697 * f_characteristic ) + ( 1.406211e-6 * f_characteristic ** 2 ) - ( 2.371512e-11 * f_characteristic ** 3 ) return self . spl >= threshold_fc
Returns an estimate of whether this AudioSegment is mostly human audible or not.
        This is done by taking an FFT of the segment and checking if the SPL of the segment
        falls below the function `f(x) = 40.11453 - 0.01683697x + 1.406211e-6x^2 - 2.371512e-11x^3`,
        where x is the most characteristic frequency in the FFT.

        Note that this method is essentially trying to determine if the estimated SPL of the segment
        falls below the threshold of human hearing, which changes over frequency. If you graph the
        threshold over different frequencies, you get what is called an audiogram. The equation above
        is derived as a curve that tries to fit a typical audiogram, specifically as found in
        Hearing Thresholds by Yost and Killion, 1997 (see https://www.etymotic.com/media/publications/erl-0096-1997.pdf).

        Sources of error are: 1) The SPL of an AudioSegment is merely an approximation; 2) this curve
        is not a perfect fit, and besides, it is only an approximation of a typical audiogram; 3) the
        algorithm uses a characteristic frequency, which is only really going to be a thing for
        short segments or for segments which are dominated by a single frequency.

        :returns: `True` if we estimate that this sound is mostly human audible. `False` if we think
                  it is not.
Returns an estimate of whether this AudioSegment is mostly human audible or not . This is done by taking an FFT of the segment and checking if the SPL of the segment falls below the function f ( x ) = 40 . 11453 - 0 . 01683697x + 1 . 406211e - 6x^2 - 2 . 371512e - 11x^3 where x is the most characteristic frequency in the FFT .