Speechdft-16-8-mono-5secs.wav (2026)

# Load with librosa (it handles 8‑bit conversion internally) y, sr_lib = librosa.load('speechdft-16-8-mono-5secs.wav', sr=16000, mono=True)

import librosa import librosa.display

y, sr = librosa.load('speechdft-16-8-mono-5secs.wav', sr=16000) speechdft-16-8-mono-5secs.wav

# Quick sanity check – plot the waveform plt.figure(figsize=(10, 2)) plt.plot(np.arange(len(audio_float))/sr, audio_float, lw=0.5) plt.title('Waveform (5 s of speech)') plt.xlabel('Time (s)') plt.ylabel('Amplitude') plt.show() a familiar “wiggly” speech trace, with a modest amount of quantisation “step‑noise” that is typical of 8‑bit audio. 3. A First‑Look Discrete Fourier Transform (DFT) The DFT is the workhorse that turns a time‑domain signal into its frequency‑domain representation. Let’s compute a single‑sided magnitude spectrum and visualise it. # Load with librosa (it handles 8‑bit conversion

S = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels, fmax=sr/2) log_S = librosa.power_to_db(S, ref=np.max) sr_lib = librosa.load('speechdft-16-8-mono-5secs.wav'