An Efficient Cascaded Filtering Retrieval Method for Big Audio Data

Abstract

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%.Index Terms-Audio middle fingerprint, big audio data, cascade filtering retrieval, content-based retrieval, Philips audio fingerprint.

Accuracy comparisons of fingerprint based song recognition approaches using very high granularity

“…Recently, some works, as papers of Yao et al [33,34], use a fingerprint extraction approach based on the technique proposed by Philips in [15] whereas Sonnleitner and Widmer [27] introduce a compact "four -dimensional", continuous hash representation of quadruples of points called quads. Although this latter approach can efficiently identify audio in large song collections, and it is robust to noise and audio quality degradation, as well as to severe distortions of speed, tempo and frequency, the generation of each "quad" appears rather complex as reported in [33] making it difficult to be used in real time applications.…”

Big Data and AI-driven Product Design: A Survey

“…Although video data is useful for product design, manual viewing can be timeconsuming, and only suitable for small data volume scenes. In comparison, intelligent video analysis supports motion detection, video summarization, video retrieval, color detection, object detection, emotion recognition, and more [221,[234][235][236] . This capability makes it possible to apply big video data in user requirement acquisition, user behavior observation, experience improvement, and product virtual display.…”

“…Big audio data [221] refers to information in the form of sound or voice. The customer center is a valuable resource for collecting users' complaints, inquiries, and suggestions throughout the product service cycle [222].…”

Big Data and AI-Driven Product Design: A Survey

“…Big audio data [219] refer to information in the form of sound or voice. The customer center is a valuable resource for collecting users' complaints, inquiries, and suggestions throughout the product service cycle [220].…”

“…Although video data is useful for product design, manual viewing can be timeconsuming, and is only suitable for small data volume scenes. In comparison, intelligent video analysis supports motion detection, video summarization, video retrieval, color detection, object detection, emotion recognition, and more [219,[232][233][234] . This capability makes it possible to apply big video data to user requirement acquisition, user behavior observation, experience improvement, and product virtual display.…”