3 files changed, 161 insertions, 0 deletions
diff --git a/tools/pcm2pdm.py b/tools/pcm2pdm.py
new file mode 100644
index 0000000..7cf05fb
--- /dev/null
+++ b/tools/pcm2pdm.py
@@ -0,0 +1,74 @@
+import numpy as np
+import soundfile
+
+def second_order_delta_sigma(pcm):
+    # Normalize input XXX: remove normalization
+    pcm = pcm / np.max(np.abs(pcm))
+
+    n = len(pcm)
+    pdm = np.zeros(n, dtype=np.uint8)
+
+    integrator1 = 0.0
+    integrator2 = 0.0
+    quantized = 0.0
+
+    for i in range(n):
+        integrator1 += pcm[i] - quantized
+        integrator2 += integrator1 - quantized
+
+        if integrator2 >= 0:
+            quantized = 1.0
+            pdm[i] = 1
+        else:
+            quantized = -1.0
+            pdm[i] = 0
+
+    return pdm
+
+def save_pdm_bin(pdm_data, file):
+    packed = np.packbits(pdm_data)
+    file.write(packed)
+
+def convert(pcm_data, oversample = 64):
+    # Oversample
+    pcm_upsampled = np.repeat(pcm_data, oversample)
+
+    # --- Convert to PDM ---
+    pdm_data = second_order_delta_sigma(pcm_upsampled)
+
+    return pdm_data
+
+def convert_file(input_file, **kwargs):
+    # --- Load audio ---
+    pcm_data, samplerate = soundfile.read(input_file)
+
+    # If stereo, use one channel
+    if pcm_data.ndim > 1:
+        pcm_data = pcm_data[:, 0]
+
+    pdm_data = convert(pcm_data, **kwargs)
+    return pdm_data
+
+
+def parse():
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Process an input file and write to an output file.')
+    parser.add_argument('-i', '--input', type=str, required=True, help='Path to the input file')
+    parser.add_argument('-o', '--output', type=str, required=True, help='Path to the output file')
+    
+    args = parser.parse_args()
+    return args
+
+def main():
+    args = parse()
+
+    out_path = args.output
+
+    pdm_data = convert_file(args.input, oversample=64)
+    with open(out_path, 'wb') as f:
+        save_pdm_bin(pdm_data, f)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/pdm2pcm.py b/tools/pdm2pcm.py
new file mode 100644
index 0000000..6d2c284
--- /dev/null
+++ b/tools/pdm2pcm.py
@@ -0,0 +1,50 @@
+
+import numpy
+import numpy as np
+from scipy.signal import firwin, lfilter, decimate
+import soundfile
+
+def load_pdm_file(filename):
+    with open(filename, 'rb') as f:
+        byte_data = np.frombuffer(f.read(), dtype=np.uint8)
+    bit_data = np.unpackbits(byte_data)
+    # Convert to +1 (for 1s) and -1 (for 0s)
+    pdm_signal = 2 * bit_data - 1
+    return pdm_signal
+
+def pdm_to_pcm(pdm_signal, decimation_factor=64):
+    # Design a low-pass filter
+    fir_filter = firwin(numtaps=101, cutoff=0.5/decimation_factor)
+    # Filter the PDM signal
+    filtered = lfilter(fir_filter, [1.0], pdm_signal)
+    # Decimate (downsample)
+    pcm_signal = filtered[::decimation_factor]
+    return pcm_signal
+
+def main():
+
+    pdm_path = 'test_tone.pdm'
+    out_path = 'output.wav'
+    pdm_data = load_pdm_file(pdm_path)
+
+    # Convert to PCM
+    oversample = 64
+    samplerate = 16000
+    pcm_data = pdm_to_pcm(pdm_data, decimation_factor=oversample)
+
+    # Normalize and save to WAV
+    pcm_data = np.expand_dims(pcm_data, axis=1)
+    #print(pcm_data.shape)
+
+    print(numpy.min(pcm_data), numpy.max(pcm_data), numpy.mean(pcm_data))
+
+    # Normalize
+    pcm_data -= 128.0
+    pcm_data = 2**15 * (pcm_data / np.max(np.abs(pcm_data)))
+
+    print(numpy.min(pcm_data), numpy.max(pcm_data), numpy.mean(pcm_data))
+
+    soundfile.write(out_path, pcm_data, samplerate)
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/testsignal.py b/tools/testsignal.py
new file mode 100644
index 0000000..0f28e1c
--- /dev/null
+++ b/tools/testsignal.py
@@ -0,0 +1,37 @@
+import numpy as np
+import soundfile as sf
+
+def generate_test_tone(
+    duration_sec=5,
+    sample_rate=44100,
+    freqs=[440, 1000, 3000],
+    noise_level=0.05,
+    amplitude=0.5,
+):
+    t = np.linspace(0, duration_sec, int(sample_rate * duration_sec), endpoint=False)
+    signal = np.zeros_like(t)
+
+    # Add multiple sine waves
+    for freq in freqs:
+        signal += amplitude * np.sin(2 * np.pi * freq * t)
+
+    # Normalize to avoid clipping if multiple frequencies are added
+    signal /= len(freqs)
+
+    # Add white noise
+    noise = noise_level * np.random.normal(0, 1, signal.shape)
+    signal += noise
+
+    return signal
+
+if __name__ == "__main__":
+
+    filename="test_tone.wav"
+    # Save to WAV file
+    
+    sample_rate = 16000
+
+    signal = generate_test_tone(sample_rate=sample_rate)
+
+    sf.write(filename, signal, sample_rate)
+    print(f"Saved test tone to '{filename}'.")