Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey <s> Summarisation <s> Listening to a speech recording is much more difficult than visually scanning a document because of the transient and temporal nature of audio. Audio recordings capture the richness of speech, yet it is difficult to directly browse the stored information. This article describes techniques for structuring, filtering, and presenting recorded speech, allowing a user to navigate and interactively find information in the audio domain. This article describes the SpeechSkimmer system for interactively skimming speech recordings. SpeechSkimmer uses speech-processing techniques to allow a user to hear recorded sounds quickly, and at several levels of detail. User interaction, through a manual input device, provides continuous real-time control of the speed and detail level of the audio presentation. SpeechSkimmer reduces the time needed to listen by incorporating time-compressed speech, pause shortening, automatic emphasis detection, and nonspeech audio feedback. This article also presents a multilevel structural approach to auditory skimming and user interface techniques for interacting with recorded speech. An observational usability test of SpeechSkimmer is discussed, as well as a redesign and reimplementation of the user interface based on the results of this usability test. <s> BIB001 </s> Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey <s> Summarisation <s> This paper deals with the automatic generation of music audio summaries from signal analysis without the use of any other information. The strategy employed here is to consider the audio signal as a succession of “states” (at various scales) corresponding to the structure (at various scales) of a piece of music. This is, of course, only applicable to certain kinds of musical genres based on some kind of repetition. From the audio signal, we first derive dynamic features representing the time evolution of the energy content in various frequency bands. These features constitute our observations from which we derive a representation of the music in terms of “states”. Since human segmentation and grouping performs better upon subsequent hearings, this “natural” approach is followed here. The first pass of the proposed algorithm uses segmentation in order to create “templates”. The second pass uses these templates in order to propose a structure of the music using unsupervised learning methods (Kmeans and hidden Markov model). The audio summary is finally constructed by choosing a representative example of each state. Further refinements of the summary audio signal construction, uses overlapadd, and a tempo detection/ beat alignment in order to improve the audio quality of the created summary. <s> BIB002 </s> Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey <s> Summarisation <s> We describe the Automatic News Summarisation and Extraction System (ANSES), which captures television news each day with the accompanying subtitles and identifies and extracts news stories from the video. Lexical chain analysis is used to provide a summary of each story and important entities are highlighted in the text. <s> BIB003 </s> Tools for Searching, Annotation and Analysis of Speech, Music, Film and Video A Survey <s> Summarisation <s> We present a novel technique for time-compressing speech, semantic compression, which uses an ASR transcript to determine which elements of the speech are presented. We carried out an exploratory user study comparing semantic compression to other novel types of time-compression techniques. We found that users feel they have a greater understanding of recordings compressed using semantic techniques than those compressed using acoustic-based techniques. An approach of using high playback speeds to indicate the location of 'insignificant' speech is not favoured by listeners, who prefer to have such segments removed from recordings. <s> BIB004
Audiovisual materials take time to hear and view, and thus there is a strong interest in summarisation There has been some, albeit limited, work on generating summaries of spoken word content. Techniques for audio alone include time compression techniques such as eliminating silence or speeding up the clip (often maintaining pitch for intelligibility) (e.g., BIB004 . Spoken word content summarisation and usability issues have been considered in some detail by BIB001 and by  . Work has also been done using errorful transcripts, by adopting techniques for general text summarisation (as is apparently the case in BIB003 . Techniques exploiting both audio and transcript include the work by Koumpis and colleagues, who use both lexical-and audio-derived prosodic information to identify elements to include in the summary  ). The same considerations have motivated research into automatic summarisation of music. The common approach is to perform a self-similarity analysis of the audio signal, often by means of a frequency-domain transformation, and then to extract those segments which are similar to other segments. These are likely to correspond to recurring passages such as the chorus of a song, and so to contain music which is salient and typical of the whole. A short segment of audio can then be constructed by stringing together characteristic extracts (see, for example, BIB002 .