M-MUSICS: an intelligent mobile music retrieval system

Abstract

Accurate voice humming transcription and efficient indexing and retrieval schemes are essential to a large-scale humming-based audio retrieval system. Although much research has been done to develop such schemes, their performance in terms of precision, recall, and F-measure, among all similarity metrics, are still unsatisfactory. In this paper, we propose a new voice query transcription scheme. It considers the following features: note onset detection using dynamic threshold methods, fundamental frequency (F0) acquisition of each frame, and frequency realignment using K-means. We use a popularity-adaptive indexing structure called frequently accessed index (FAI) based on frequently queried tunes for indexing purposes. In addition, we propose a semi-supervised relevance feedback and query reformulation scheme based on a genetic algorithm to improve retrieval efficiency. In this paper, we extend our efforts to mobile multimedia environments and develop a mobile audio retrieval system. Experiments show our system performs satisfactory in wireless mobile multimedia environments.

Visual saliency models for summarization of diagnostic hysteroscopy videos in healthcare systems

“…Another possible future direction is to combine data hiding [watermarking (Liu et al 2015 ; Liu et al 2016 ), image and video steganography (Mstafa and Elleithy 2015 ; Muhammad et al 2015 ; Lin et al 2015 )] with the video summarization frameworks by embedding the patient and gynecologists data in DH videos/keyframes, resulting in secure and privacy-preserving VS framework as presented in ( Muhammad et al 2015 ) for secure visual contents retrieval from personalized repositories and other mobile healthcare applications (Lv et al 2016 ). Furthermore, we are also planning to explore deep learning and incorporate GPUs based processing (Mei and Tian 2016 ; Mei 2014 ) for efficient keyframes extraction, their indexing and retrieval (Rho et al 2008 ; Rho et al 2011 ; Rho and Hwang 2006 ).…”

From IR to Mobile IR

“…Finally, a different kind of IR, related to query by humming, is reported by Rho et al [154]. The target of the paper is music retrieval, where human voice is used to produce a short clip of singing, whistling or humming to give a rough approximation of the music requested.…”