more frontend utils

third_party/paddle_audio/frontend/common.py

@@ -98,6 +98,22 @@ def dft_matrix_fast(n_fft:int, winlen:int=None, n_bin:int=None) -> Tuple[np.ndar
     return w_real, w_imag, kernel_size
     
 
+def bin2hz(bin:Union[List[int], np.ndarray], N:int, sr:int)->List[float]:
+    """FFT bins to Hz.
+    
+    http://practicalcryptography.com/miscellaneous/machine-learning/intuitive-guide-discrete-fourier-transform/
+
+    Args:
+        bins (List[int] or np.ndarray): bin index.
+        N (int): the number of samples, or FFT points.
+        sr (int): sampling rate.
+
+    Returns:
+        List[float]: Hz's.
+    """
+    hz = bin * float(sr) / N
+        
+        
 def hz2mel(hz):
     """Convert a value in Hertz to Mels
 
@@ -106,6 +122,7 @@ def hz2mel(hz):
     """
     return 1127 * np.log(1+hz/700.0)
 
+
 def mel2hz(mel):
     """Convert a value in Mels to Hertz
 
@@ -113,3 +130,72 @@ def mel2hz(mel):
     :returns: a value in Hertz. If an array was passed in, an identical sized array is returned.
     """
     return 700 * (np.exp(mel/1127.0)-1)
+
+
+
+def rms_to_db(rms: float):
+    """Root Mean Square to dB.
+
+    Args:
+        rms ([float]): root mean square
+
+    Returns:
+        float: dB
+    """
+    return 20.0 * math.log10(max(1e-16, rms))
+
+
+def rms_to_dbfs(rms: float):
+    """Root Mean Square to dBFS.
+    https://fireattack.wordpress.com/2017/02/06/replaygain-loudness-normalization-and-applications/
+    Audio is mix of sine wave, so 1 amp sine wave's Full scale is 0.7071, equal to -3.0103dB.
+   
+    dB = dBFS + 3.0103
+    dBFS = db - 3.0103
+    e.g. 0 dB = -3.0103 dBFS
+
+    Args:
+        rms ([float]): root mean square
+
+    Returns:
+        float: dBFS
+    """
+    return rms_to_db(rms) - 3.0103
+
+
+def max_dbfs(sample_data: np.ndarray):
+    """Peak dBFS based on the maximum energy sample. 
+
+    Args:
+        sample_data ([np.ndarray]): float array, [-1, 1].
+
+    Returns:
+        float: dBFS 
+    """
+    # Peak dBFS based on the maximum energy sample. Will prevent overdrive if used for normalization.
+    return rms_to_dbfs(max(abs(np.min(sample_data)), abs(np.max(sample_data))))
+
+
+def mean_dbfs(sample_data):
+    """Peak dBFS based on the RMS energy. 
+
+    Args:
+        sample_data ([np.ndarray]): float array, [-1, 1].
+
+    Returns:
+        float: dBFS 
+    """
+    return rms_to_dbfs(
+        math.sqrt(np.mean(np.square(sample_data, dtype=np.float64))))
+
+
+def gain_db_to_ratio(gain_db: float):
+    """dB to ratio
+
+    Args:
+        gain_db (float): gain in dB
+
+    Returns:
+        float: scale in amp
+    """
+    return math.pow(10.0, gain_db / 20.0)