An Efficient Cascaded Filtering Retrieval Method for Big Audio Data

Fast audio retrieval is crucial for many important applications and yet demanding due to the high dimension nature and increasingly larger volume of audios on the Internet. Although audio fingerprinting can greatly reduce its dimension while keeping audio identifiable, the dimension for audio fingerprints is still too high to scale up for big audio data. The tradeoff between accuracy (measured by precision and recall rate) and efficiency (measured by retrieval time) prevents further reduction in the dimension of fingerprints. This paper shows that a multi-stage filtering strategy can achieve both speedup and high accuracy, with the beginning stages focusing on speedup and the end stage emphasizing accuracy. With this strategy, an efficient cascaded filtering retrieval method is proposed that consists of filtering with Fibonacci hashing, the middle fingerprint, thresholds to quickly select candidate audios, and refining with an accurate and robust fingerprint on the candidate audios. Experiments with 500 000 audios show that the proposed method can achieve a speed gain more than 28 K times that of the Fibonacci hashing retrieval. After applying MP3 conversion, resampling, white noise addition, and background noise addition, the recall rates of the method are all above 99.45%, and the precision is the same as the Philips audio fingerprint, which is close to 100%.