To design an accurate query-by-humming system, focus on three core components: robust audio processing, effective feature extraction, and a flexible matching algorithm. The system must convert a hummed input into a structured representation that can be efficiently compared to a database of known melodies. This involves handling variations in pitch, tempo, and recording quality while maintaining computational efficiency.
First, process the raw audio input to isolate the melody. Use noise reduction techniques like spectral subtraction to minimize background interference, and apply pre-emphasis filters to enhance higher frequencies critical for pitch detection. Convert the signal into a pitch contour using time-frequency analysis (e.g., Short-Time Fourier Transform) paired with pitch detection algorithms like YIN or autocorrelation. For example, YIN identifies fundamental frequencies by analyzing periodicity in the waveform, which works well even with imperfect humming. Next, segment the pitch sequence into discrete notes by detecting stable pitch regions and transitions. Convert frequencies to MIDI note numbers and derive relative pitch intervals (e.g., +2 semitones for a major second) to normalize transpositions. Rhythmic features like note durations and inter-onset intervals should also be extracted and normalized by tempo, such as scaling all durations to a common beats-per-minute (BPM) baseline.
The matching algorithm must compare the extracted features to a database of reference tracks. Use dynamic time warping (DTW) to align the query’s pitch and rhythm sequences with candidate melodies, accommodating tempo variations. For example, DTW can stretch or compress time axes to find the optimal alignment between the hummed query and a stored melody. Combine this with a similarity metric, such as cosine similarity for pitch intervals or edit distance for rhythmic patterns. To improve efficiency, index the database using techniques like n-gram hashing of note sequences or locality-sensitive hashing (LSH) for approximate matches. For instance, breaking melodies into 3-note subsequences (trigrams) allows fast lookups even if the query is incomplete or contains errors. Additionally, train a machine learning model (e.g., a Siamese neural network) to learn robust similarity scores from labeled pairs of hummed queries and ground-truth melodies, which can handle subtle pitch inaccuracies better than rule-based methods.
Finally, enhance robustness by designing for common user errors. For pitch variations, allow a tolerance window (e.g., ±1 semitone) during note matching. To address rhythm inconsistencies, use beat-tracking algorithms to normalize the tempo of the query before comparing it to the database. Implement feedback mechanisms: if a user rejects a match, log the discrepancy to retrain models or adjust similarity thresholds. For example, if users consistently hum a specific song with a flattened third note, the system could learn to prioritize matches with that deviation. Regularly update the database with new entries and user-corrected examples to improve accuracy over time. By combining signal processing, adaptive matching, and iterative learning, the system can reliably map imperfect hums to correct melodies.