Recall is a performance metric used to evaluate the effectiveness of search applications, including audio search. In simple terms, recall measures the ability of a system to retrieve all relevant items from a dataset. It answers the question: "Of all the relevant items that could have been found, how many did the system actually return?" Recall is expressed as a percentage, calculated by dividing the number of true positive results (correctly identified relevant items) by the total number of relevant items available in the dataset.
In the context of audio search applications, recall can play a significant role. For example, consider an application designed to find specific audio clips within a large library, such as music tracks, podcasts, or speeches. If a developer is building a system to help users search for segments containing certain keywords or phrases, achieving high recall means that the system should return most, if not all, of the audio clips that contain those keywords. If out of 100 clips that contain the search term, the system retrieves 80, it has a recall of 80%.
It's important for developers to balance recall with precision, which measures the accuracy of the results returned. High recall might lead to including irrelevant items, making it harder for users to find precisely what they want. Therefore, when designing an audio search application, developers should focus on methods like improving search algorithms, employing effective indexing, and enhancing the system's ability to understand context to maximize recall while maintaining a good level of precision.