A survey on content-based retrieval for multimedia databases <s> QBE for Audio Retrieval <s> Many audio and multimedia applications would benefit from the ability to classify and search for audio based on its characteristics. The audio analysis, search, and classification engine described here reduces sounds to perceptual and acoustical features. This lets users search or retrieve sounds by any one feature or a combination of them, by specifying previously learned classes based on these features, or by selecting or entering reference sounds and asking the engine to retrieve similar or dissimilar sounds. <s> BIB001 </s> A survey on content-based retrieval for multimedia databases <s> QBE for Audio Retrieval <s> Despite growing interest in multimedia data management, audio retrieval has received little attention. In part, this can be attributed to existing unstructured audio representations that do not easily lend themselves to content based retrieval and especially browsing. This paper aims to address this oversight. It begins by reviewing existing techniques and the specific problems posed by unstructured representations. Some characteristics of audio perception that may be exploited in the solution to these problems are then presented. A new structured representation is then detailed that is designed to support content based retrieval and browsing. Finally, the suitability of this representation for its intended purpose is discussed. <s> BIB002
QBE experiences for audio data are much less than those for images or video databases. One of the reasons is that there are difficulties in recognizing or extracting the contents of an audio object. However, some studies have presented QBE applications to audio data. Ghias et al.  is one of the novel approach of QBE for audio data, specifically, the musical data of songs. A person humming is entered to the system as an example of a musical phrase. The system retrieves the melody of songs which match the given example. Humming as the way of representing a query condition for music retrieval is quite intuitive to a general user. Specifying melodies in other forms of representation such as a score is not easy. Wold et al. BIB001 discusses content-based retrieval of generic audio data, by means of QBE as well as the retrieval by attributes attached to sound data such as keyword, sampling rate, or date of creation. It takes the approach of extracting basic elements called an analysis feature vector, which consists of duration, pitch, amplitude, brightness, and bandwidth. Melih and Gonzalez BIB002 describes an on-going study of content-based retrieval of audio, which mainly discusses an audio signal processing method of extracting features for evaluating peculiarity of individual sound. Content-based retrieval of audio is an area less mature than content-based retrieval studies for image or video. This comes from the difficulty of extracting features of audio which clearly shows the peculiarity of individual sound source.