Speechdft-16-8-mono-5secs.wav -

import librosa import librosa.display

# Frequency axis (Hz) freqs = np.fft.rfftfreq(N, d=1/sr) speechdft-16-8-mono-5secs.wav

import numpy as np from scipy.io import wavfile import matplotlib.pyplot as plt import librosa import librosa

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) fmax=sr/2) log_S = librosa.power_to_db(S

# ------------------------------------------------- # 3️⃣ Compute the DFT (via FFT) – only the positive frequencies # ------------------------------------------------- N = len(audio_float) # number of samples = 5 s × 16 kHz = 80 000 fft_vals = np.fft.rfft(audio_float) # real‑valued FFT → N/2+1 points fft_mag = np.abs(fft_vals) / N # normalise magnitude

import librosa import librosa.display