This paper proposes shot genre classification from MPEG compressed movies, as one of the high-level indexing methods for audio-visual contents. Through statistical analysis of low-level and mid-level audio-visual features on compressed domain, the proposed method can achieve subjectively accurate shot classification within the movies into predefined genre set, which can be applied to various co...

This paper presents an automatic genre classification model that implements a flexible classification scheme, i.e. a scheme capable of performing zero-, oneor multi-genre assignment. I suggest that this scheme is more appropriate for genres on the web, because many web pages have often more than one genre or none at all. The model that I propose relies on the distinction between the concepts of...

Recurrent neural network language models (RNNLMs) have consistently outperformed n-gram language models when used in automatic speech recognition (ASR). This is because RNNLMs provide robust parameter estimation through the use of a continuous-space representation of words, and can generally model longer context dependencies than n-grams. The adaptation of RNNLMs to new domains remains an activ...

In this paper, we discuss the notion of genre as a basis for addressing the problem of context representation in digital preservation. We outline several reference points for the notion of genre. This includes a review of diplomatic principles that can support and enhance the power of genre as a key to capture information about context relations. Further, we discuss the impact of open genre mod...

Modern search engines are typically queried with keywords, which foremostly convey the topic of the sought web page. Consequently the resulting top hits are often topically relevant, but nonetheless not what the user wants. The premise of this paper is that the relevance of the hits can be improved when also searching by genre, classification criterion orthogonal to topic. To this end a genre c...

This paper examines the potential of high-level features extracted from symbolic musical representations in regards to musical classification. Twenty features are implemented and tested by using them to classify 225 MIDI files by genre. This system differs from previous automatic genre classification systems, which have focused on low-level features extracted from audio data. Files are classifi...

In this paper, two experiments in automatic genre classification of web pages are presented. These two experiments are designed to highlight three important issues related to genre classification: corpus composition and genre palettes, feature representativeness, and exportability of classification models. Results show the influence of corpus composition and genre palette on classification rate...

We explore the integration of multiple factors such as genre and speaker gender for acoustic model adaptation tasks to improve Mandarin ASR system performance on broadcast news and broadcast conversation audio. We investigate the use of multifactor clustering of acoustic model training data and the application of MPE-MAP and fMPE-MAP acoustic model adaptations. We found that by effectively comb...