Audio search systems process different audio formats through a combination of decoding, feature extraction, and indexing. First, when a user uploads an audio file in formats like MP3, WAV, or AAC, the system must decode the file to convert it into a format that can be easily processed. Each audio format has its own encoding scheme, so the system utilizes specific libraries or tools, such as FFmpeg, to handle the decoding step. This ensures that regardless of the original format, the audio can be read and processed consistently.
Once the audio data has been decoded, the next step is feature extraction. This involves analyzing the audio signal to derive meaningful characteristics that can be used for search purposes. Common features derived from audio include spectrograms, mel-frequency cepstral coefficients (MFCCs), or even text transcripts if speech recognition is applied. For example, in a system that searches for music tracks, features like tempo, key, and timbre might be extracted to create an audio fingerprint. This fingerprint can then be used for matching audio queries against a database of indexed audio.
Finally, the indexed features are stored in a database which allows for quick retrieval. When a search query is initiated, the system compares the extracted features of the query audio with those stored in the database using various algorithms, like nearest neighbor searches. This process ensures that the system can handle searches efficiently, regardless of the original audio format. For instance, a user could upload an AAC file to search for a similar WAV file in the system, and thanks to the aforementioned processes, the search will work seamlessly across different formats.