How do you compute the F1 score for audio search evaluation?

To compute the F1 score for audio search evaluation, you need to balance precision and recall, two metrics that measure the quality of search results. Precision calculates the proportion of correctly identified matches (true positives) among all results returned by the system. Recall measures how many of the true relevant items in the dataset were successfully retrieved. The F1 score is the harmonic mean of these two values, providing a single metric that balances both concerns. For example, if a search returns 8 audio clips, 5 of which are correct (true positives), and there are 10 relevant clips in total, precision is 5/8 (62.5%), recall is 5/10 (50%), and the F1 score is 2(0.6250.5)/(0.625+0.5) ≈ 55.5%. This ensures neither metric is overlooked.

The calculation process involves three steps. First, define the ground truth: a set of audio files known to be relevant for a specific query. Second, run the search and compare results against the ground truth to count true positives (correct matches), false positives (incorrect matches), and false negatives (missed matches). For instance, if a query should return 10 songs but the system retrieves 7 correct and 3 incorrect, with 3 relevant songs missed, precision is 7/10 (70%), recall is 7/10 (70%), and F1 is 70%. Finally, repeat this for multiple queries and average the F1 scores (macro-averaging) or aggregate all counts first (micro-averaging). Macro-averaging treats each query equally, while micro-averaging weights results by their frequency.

Practical considerations include defining relevance criteria and handling thresholds. Audio search systems often use similarity scores to rank results, and adjusting the threshold for what counts as a “match” impacts precision and recall. For example, a lower threshold might increase recall (fewer missed matches) but reduce precision (more false positives). Developers must also clarify what constitutes a “relevant” result—exact matches, remixes, or covers. Additionally, F1 ignores ranking order, so systems prioritizing correct results at the top of a list might require complementary metrics like mean average precision. Clear ground truth labeling and consistent evaluation across queries are critical to ensure reliable F1 scores.