The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A new algorithm for content-based audio information retrieval is introduced in this work. Assuming that there exist hidden acoustic topics and each audio clip is a mixture of those acoustic topics, we proposed a topic model that learns a probability distribution over a set of hidden topics of a given audio clip in an unsupervised manner. We use the Latent Dirichlet Allocation (LDA) method for the...
Automatic acoustic scene classification of real life, complex and unstructured acoustic scenes is a challenging task as the number of acoustic sources present in the audio stream are unknown and overlapping in time. In this work, we present a novel approach to classification such unstructured acoustic scenes. Motivated by the bottom-up attention model of the human auditory system, salient events of...
In the recently proposed latent perceptual indexing of audio, a collection of clips is indexed using unit-document frequency measures between a set of reference clusters as units and the clips as the documents. The reference units are derived by clustering the bag-of-feature vectors extracted from the whole audio library using an unsupervised clustering technique. Indexing is achieved through reduced-rank...
Using the recently proposed framework for latent perceptual indexing of audio clips, we present classification of whole clips categorized by two schemes: high-level semantic labels and the mid-level perceptually motivated onomatopoeia labels. First, feature-vectors extracted from the clips in the database are grouped into reference clusters using an unsupervised clustering technique. A unit-document...
We present a query-by-example audio retrieval framework by indexing audio clips in a generic database as points in a latent perceptual space. First, feature-vectors extracted from the clips in the database are grouped into reference clusters using an unsupervised clustering technique. An audio clip-to-cluster matrix is constructed by keeping count of the number of features that are quantized into...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.