Recording and Processing Personal Voice Signals with Digital Filtering

First, record a personal voice signal and sample it to ensure sufficient data acquisition. In MATLAB implementation, this typically involves using the audiorecorder function for recording and specifying sampling frequency (e.g., 8 kHz or 44.1 kHz) and bit depth. Next, plot the time-domain waveform and spectrogram of the sampled signal using functions like plot() for waveform visualization and spectrogram() for frequency analysis, helping characterize signal features through time-frequency representation. Before filter design, establish precise performance indicators including passband/stopband frequencies, ripple tolerances, and attenuation requirements. For filter implementation, employ the window function method (using hamming() or kaiser() windows with fir1()) for FIR filters, or bilinear transform (via butter() or cheby1() functions) for IIR filters. After design, visualize the filter's frequency response using freqz() to evaluate magnitude/phase characteristics. Apply the custom-designed filter to the original voice signal using filtering operations (filter() for IIR or conv() for FIR implementations). Generate comparative plots of filtered signals' time-domain waveforms and spectrograms. Analyze changes in signal characteristics by examining frequency component suppression/enhancement and time-domain modifications. Implement audio playback using sound() or audiowrite() functions to verify filtering effectiveness and output clarity. Finally, create a user-friendly signal processing system interface using MATLAB App Designer or GUIDE, incorporating interactive controls for real-time parameter adjustment, visualization updates, and automated processing pipelines.