Classification of Overlapped Audio Events Based on AT, PLSA, and the Combination of Them

Audio event classification, as an important part of Computational Auditory Scene Analysis, has attracted much attention. Currently, the classification technology is mature enough to classify isolated audio events accurately, but for overlapped audio events, it performs much worse. While in real life, most audio documents would have certain percentage of overlaps, and so the overlap classification problem is an important part of audio classification. Nowadays, the work on overlapped audio event classification is still scarce, and most existing overlap classification systems can only recognize one audio event for an overlap. In this paper, in order to deal with overlaps, we innovatively introduce the author-topic (AT) model which was first proposed for text analysis into audio classification, and innovatively combine it with PLSA (Probabilistic Latent Semantic Analysis). We propose 4 systems, i.e. AT, PLSA, AT-PLSA and PLSA-AT, to classify overlaps. The 4 proposed systems have the ability to recognize two or more audio events for an overlap. The experimental results show that the 4 systems perform well in classifying overlapped audio events, whether it is the overlap in training set or the overlap out of training set. Also they perform well in classifying isolated audio events

UNDERSTANDING MUSIC TRACK POPULARITY IN A SOCIAL NETWORK

Thousands of music tracks are uploaded to the Internet every day through websites and social networks that focus on music. While some content has been popular for decades, some tracks that have just been released have been ignored. What makes a music track popular? Can the duration of a music track’s popularity be explained and predicted? By analysing data on the performance of a music track on the ranking charts, coupled with the creation of machine-generated music semantics constructs and a variety of other track, artist and market descriptors, this research tests a model to assess how track popularity and duration on the charts are determined. The dataset has 78,000+ track ranking observations from a streaming music service. The importance of music semantics constructs (genre, mood, instrumental, theme) for a track, and other non-musical factors, such as artist reputation and social information, are assessed. These may influence the staying power of music tracks in online social networks. The results show it is possible to explain chart popularity duration and the weekly ranking of music tracks. This research emphasizes the power of data analytics for knowledge discovery and explanation that can be achieved with a combination of machine-based and econometrics-based approaches

Topic modelling for routine discovery from egocentric photo-streams

Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals' lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed

Real-Time Monophonic and Polyphonic Audio Classification from Power Spectra

International audienceThis work addresses the recurring challenge of real-time monophonic and polyphonic audio source classification. The whole normalized power spectrum (NPS) is directly involved in the proposed process, avoiding complex and hazardous traditional feature extraction. It is also a natural candidate for polyphonic events thanks to its additive property in such cases. The classification task is performed through a nonparametric kernel-based generative modeling of the power spectrum. Advantage of this model is twofold: it is almost hypothesis free and it allows to straightforwardly obtain the maximum a posteriori classification rule of online signals. Moreover it makes use of the monophonic dataset to build the polyphonic one. Then, to reach the real-time target, the complexity of the method can be tuned by using a standard hierarchical clustering preprocessing of the prototypes, revealing a particularly efficient computation time and classification accuracy trade-off. The proposed method, called RARE (for Real-time Audio Recognition Engine) reveals encouraging results both in monophonic and polyphonic classification tasks on benchmark and owned datasets, including also the targeted real-time situation. In particular, this method benefits from several advantages compared to the state-of-the-art methods including a reduced training time, no feature extraction, the ability to control the computation - accuracy trade-off and no training on already mixed sounds for polyphonic classification