This project is designed to analyze the tempo (BPM) of .wav music files, utilizing various digital signal processing techniques. The primary method used for tempo detection in this repository is implemented in the CombFilterAnalyzer, which is giving high precision in determining BPM.

The Analyzer is making use of an algorithm, which was proposed on this page.

The algorithm begins by dissecting the audio signal into distinct frequency bands, isolating different instrumental ranges. This step is important, as it mitigates the potential for tempo detection errors caused by overlapping beats from various instruments. By applying the Fast Fourier Transform (FFT) and segmenting the resultant spectrum into predefined frequency ranges, each band captures a unique aspect of the music's profile (0-200Hz to 3200Hz). This is ensuring a comprehensive analysis across the spectrum.

Each frequency band undergoes full-wave rectification followed by a convolution with an optional window function (a process for smoothing out the signal and accentuating the amplitudes). This smoothing helps to have a cleaner representation of the rhythmic pulse.

The algorithm now differentiates the signals to highlight sudden changes in amplitude. By differentiating and then half-wave rectifying, the algorithm determines significant sound intensity increases, which typically align with the beats in music. This step transforms the smoothed envelopes into a form optimized for the final tempo analysis.

Finally, the algorithm uses a comb filter to scan through the differentiated signals. This comb filter is convolved with the signal to determine the alignment between the signal's rhythmic pattern and the filter's tempo. When the tempo of the comb filter resonates with the tempo of the music, the convolution results in a signal with pronounced peaks, indicating a strong correlation. By examining the energy output of these convolutions across a spectrum of tempos, the algorithm can accurately determine the music's tempo.