Fingerprints are one of the most unique visual patterns on human body. It represents both innate and acquired identities of an individual. In this paper, we focus on relationship between fingerprint patterns and human identities by transforming image into audio. Digiti Sonus, an interactive fingerprint sonification installation, contains a novel idea to facilitate and enhance an interactive auditory meaning by transforming user-intended fingerprint expression into audio spectrogram. In order to enable personalized sonification, the installation employed dynamic filter generation based on minutiae extraction using core-invariant scanning method and image skeletonization.

Music evokes various human emotions or creates music moods through low level musical features. In fact, typical music consists of one or more moods and this can be used as an important factor for determining the similarity between music. In this paper, we propose a new music retrieval scheme based on the mood change pattern. For this, we first divide music clips into segments based on low level musical features. Then, we apply K-means clustering algorithm for grouping them into clusters with similar features. By assigning a unique mood symbol for each group, each music clip can be represented into a sequence of mood symbols. Then, we estimate the similarity of music based on the similarity of their musical mood sequence using the longest common subsequence (LCS) algorithm. To evaluate the performance of our scheme, we carried out various experiments and measured the user evaluation. We report some of the results.

To date, common acoustic features such as MPEG-7 and Fourier/wavelet transform-based features have been frequently used for environmental sound classification. However, these transforms have difficulty dealing with specific properties of environmental sounds, due to their limited scopes. In this paper, we investigate three types of transforms as yet untried for this purpose, and show that they are more effective than traditional features. This result is mainly due to the fact that they have functionalities that were not easily treatable with traditional transforms. Experimental results show that the combination of these features with traditional features can achieve 86.09% of the maximum accuracy in environmental sound classification, compared to 74.35% of the maximum accuracy when confined to traditional features.

With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.

Query reformulation has been suggested as an effective way to improve retrieval efficiency in text information retrieval and one of the well-known techniques for query reformulation is user relevance feedback. Recently, there has been an increased interest in the query reformulation using relevance feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have still not been exploited widely in the field of music retrieval. In this paper, we propose a novel music retrieval scheme that is based on user relevance feedback with genetic algorithm and evolutionary method with neural network. The former is for reformulating a user query and the latter is for reducing the population size by learning neural network. We implemented a prototype music retrieval system called MUSEMBLE based on this scheme. Experimental results showed that our proposed scheme achieves a good performance.

Accurate voice humming transcription and efficient indexing schemes are essential for a large-scale humming-based music retrieval system. Although many researches have been done to develop such schemes, their performances are not still satisfactory. In our previous works, we proposed (i) a new voice query transcription scheme [4], (ii) a popularity-adaptive indexing structure called FAI [6] for fast retrieval, and (iii) a semi-supervised relevance feedback and query reformulation scheme based on a genetic algorithm [7] in order to improve retrieval efficiency. In this demonstration, we extend our efforts to a mobile environment and develop a prototype mobile music retrieval system called M-MUSICS. Our focus in this implementation includes versatile user interface for easy querying and browsing on a typical mobile device such as PDA phone and satisfactory performance in a wireless mobile environment. We report some of the results.