Aggregate features and ADABOOST for music classification We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features. Personal computers and portable digital music players are increasingly popular platforms for storing and playing music. (Apple alone has sold more than 42 million iPod players and more than a billion songs from their iTunes Store.) As personal music collections continue to grow, the task of organizing and selecting music becomes more challenging. This motivates research in automatic methods for search, classification, and recommendation.. We deal here with one aspect of this challenge, the prediction of the genre or artist of a song based on features extracted from its audio waveform. This task is challenging for at least two reasons. First, the raw musical signal is very high dimensional. In CD quality audio, a single monophonic waveform contains 44100 values per second. Thus a stereo 3 minute song contains nearly 16 million samples. More compact acoustic features can be extracted from the waveform. However, many of these are computed over short frames of audio, thus reducing the degree of compression achieved. Second, music databases can be very large. The website www.itunesregistry.com recently reported an average iTunes collection size of 3,542 songs. Commercial databases for online music services can contain 500,000 or more (personal communication, Benjamin Masse, president radiolibre.ca). To be useful for real world applications, a classifier must scale to large datasets.. In this paper we present a genre and artist classifier that addresses these issues. We use the ensemble learner ADABOOST, which performs large-margin classification by iteratively combining the weighted votes of a collection of weak learners. This approach has two advantages in the context of the challenges we face. First, our model uses simple decision stumps, each of which operates on a single feature dimension. Thus our model performs feature selection in parallel with classification. Although a feature set with redundant or non-informative features will slow down computation, those features will be left behind by ADABOOST as it iteratively selects informative features. Second, ADABOOST scales linearly with the number of training points provided. If we limit the number of learning iterations, our model has the potential to deal with very large datasets. This compares favorably with other state-of-the-art large margin classifiers such as SVMs, which have an overall computational complexity that is quadratic in the number of data points. This paper makes two contributions. First we provide experimental evidence that our algorithm is competitive with other state-of-the-art classifiers for genre and artist recognition. Second we explore the issue of feature extraction, focusing on the challenge of aggregating frame-level acoustic features into a form suitable for classification. We analyze experiments on several classifiers using different aggregation segment sizes. The results support the general claim that aggregation is a useful strategy, and suggest reasonable limits on segment size.. The paper is structured as follows: in Section 2 we discuss previous attempts at music genre and artist recognition, focusing separately on feature extraction, feature aggregation and classification. In Section 3 we describe our ADABOOST model and aggregation technique. In Section 4 we provide experimental results for our model. This section includes a discussion of the results of the MIREX (Music Information Retrieval Evaluation eXchange) 2005 genre prediction contest and artist recognition contests where our algorithms won first and second prizes, respectively. In Section 5 we present the experimental results from our investigation of segment length for feature aggregation. Finally in Section 6 we offer concluding remarks and discuss future work. In this work, we have presented a supervised learning algorithm for classifying recorded music. We have demonstrated with results on three databases that this algorithm is effective in genre prediction; to our knowledge, at the time of writing these results are the highest genre prediction rates published for each of Tzanetakis' database, Magnatune, and for the USPOP database. We attribute this effectiveness to our particular strategy for aggregating frame-level, and to the use of ADABOOST to produce a classifier.. Recent work by Lippens et al. (2004) and an earlier work by Soltau (1997) (described in Aucouturier and Pachet (2003) ) suggest standards to which we may hold these results. Lippens et al. (2004) conducted a study in which people were asked to do genre-recognition on a small dataset involving six quite different genres. It was found that on average, people were correct only around 90% of the time. In a Turing-test conducted by Soltau (1997) , he finds that his automatic music classifier is similar in performance to 37 subjects who he trained to distinguish rock from pop using the same training and test sets. Given the steady and significant improvement in classification performance since 1997, we wonder if automatic methods are not already more efficient at learning genres than some people.. Future work would explore the value of segmentation when additional frame-level features act on longer frames to compute rhythmic fingerprints and short-term timbre dynamics. This includes evaluating features extracted from the beat histogram and the autocorrelation phase matrix Eck and Casagrande (2005) . We could also enrich the label set. Although in our experiments we considered a single label for each observation (each song belongs to one and only one category), it is more realistic to use hierarchical, or generally overlapping class labels for labeling music by its style. ADABOOST.MH extends naturally to multi-label classification by allowing the label vectors to contain more than one positive entry.. Music classification algorithms are approaching a level of maturity where they can aid in the hand labeling of data, and can verify large labeled collections automatically. However, to be useful in a commercial setting, these algorithms must run quickly and be able to learn from training sets that are one, two, even three orders of magnitude larger than the ones dealt with in our experiments. To this end, it would be useful to establish large databases of music that can be legally shared among researchers. The overall performance of each entry was calculated by averaging the raw classification accuracy on USPOP with a hierarchical classification accuracy on Magnatune.. 6 Table 2 and Table 3 summarize the contest results. In this section we describe a genre and artist classifier based on multiclass ADABOOST. In keeping with the organization of Section 2, we organize this section around feature extraction, feature aggregation, and classification.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O


P-FCM: a proximity-based fuzzy clustering for user-centered web applications In last years, the Internet and the web have been evolved in an astonishing way. Standard web search services play an important role as useful tools for the Internet community even though they suffer from a certain difficulty. The web continues its growth, making the reliability of Internet-based information and retrieval systems more complex. Nevertheless there has been a substantial analysis of the gap between the expected information and the returned information, the work of web search engine is still very hard. There are different problems concerning web searching activity, one among these falls in the query phase. Each engine provide an interface which the user is forced to learn. Often, the searching process returns a huge list of answers that are irrelevant, unavailable, or outdated. The tediosity of querying, due to the fact the queries are too weak to cope with the user's expressiveness, has stimulated the designers to enrich the human-system interaction with new searching metaphors. One of these is the searching of ''similar'' pages, as offered by Google, Yahoo and others. The idea is very good, since the similarity gives an easy and intuitive mechanism to express a complex relation. We believe that this approach could become more effective if the user can rely on major flexibility in expressing the similarity dependencies with respect the current and available possibilities. In this paper we introduce a novel method for considering and processing the user-driven similarity during web navigation. We define an extension of fuzzy C-means algorithm, namely proximity fuzzy C-means (P-FCM) incorporating a measure of similarity or dissimilarity as user's feedback on the clusters. We present the theoretical framework of this extension and then we observe, through a suite of webbased experiments, how significant is the impact of user's feedback during P-FCM functioning. These observations suggest that the P-FCM approach can offer a relatively simple way of improving the web page classification according with the user interaction with the search engine. With a magnitude measured in dozen and dozen of terabyte of data, the web is not an easy medium to master. Most of web search engines explore a certain portion of the web by indexing most frequent words in all HTML files they can find (search based on the content of documents) and by storing them into a database. Users write a query containing keywords related to the subject they want to search, the engine consults the database and provide results as a linear list of documents containing these words, typically ranked in order of relevance. Indexing is the most common approach to information discovery but the keyword matching provides just one ingredient to finding the right Web pages. The unsatisfactory aspect of this approach is that the list can be quite long, with many replications, and without any indication of possible grouping of related material. Evaluation functions of these engines are usually poor, so documents placed on the top of the result list are often less acceptable than lower ones. If the list is long, one might never reach the better documents, because there is a strong probability that the user gets bored after clicking on several hundred links and gives up. For this reason, in these last years, many search engines moved from (or at least combined) indexing to categorization. In the last years of 1990s Lycos, Yahoo, Altavista, Netscape and Lycos, with many others engines, maintain a categorization of Web pages represented by a position within a knowledge hierarchy. This approach allows a more focused search restricted to the documents within a given category and also the results of a query are arranged within subcategories. This solution is not lacking of drawbacks: first, for many search engines this task is based on manual categorization of documents performed by a team of thousand of well-trained categorization technicians. It is questionable whether manual classification will be able to scale well with the growth of the web. Furthermore, the consistency of categorization is hard to maintain when different human classifiers are involved. An experimental study on manual indexing for information retrieval systems has shown that the degree of overlap in the keywords selected by two similarly trained people to represent the same document is, on average, no higher than 30% [4] .. Even though it is still unfeasible to fully extract the meaning of a HTML document, clustering provides a powerful tool to categorize the document without a deep, a priori knowledge of categories (unsupervised clustering). There are many clustering algorithms where the similarity measure is a distance (or objective) function that is iteratively minimized to obtain the final partition. The objective function is chosen depending on the geometric structure of the cluster [9] . Fuzzy C-means clustering (FCM) algorithm, proposed by Bezdek [2] , and other researchers can provide an unsupervised approach to the cluster analysis of data. There have been a number of interesting developments along the main line see [5, 7, 8, 12] . In general, clustering is essentially devoted to partitioning a set of entities into a number of homogeneous clusters, with respect to a suitable similarity measure; clusters of web resources are useful to formulate (or reformulate) the query and to drive a search for ''similar'' documents on the web (without considering the utility of clustering in bookmark organization and user profile management). The proposed generalized version of the clustering can be positioned in the realm of knowledge-based clustering cf. [1] . An important facet of this emerging paradigm is to seamlessly combine two sources of information: the one residing within the data set itself (and to be revealed through some optimization process) and the second one coming from the user/designer who instills some general observations (hints) as to the nature of the data while being placed in a general context of the problem at hand. These two sources of information need to be reconciled and put into some sort of collaborative environment. The form of such environment and the ensuing algorithmic details are the crux of the approach proposed in this study. Here we are more specific as we primarily look at the specific type of hints augmenting the data-driven clustering in the form of proximity between pairs of data (patterns). Relational clustering by [6] can be viewed as one alternative approach. The notation being used throughout the study is standard: patterns are located in an n-dimensional space of reals R n , we are concerned with ''N'' patterns to be clustered into ''c'' clusters. While the approach could be used to augment a vast array of clustering techniques (which eventually may call for some technical modifications with the essence being fully retained), we confine ourselves to the FCM method. There are two basic reasons for that. First, this algorithm is widely known so that any enhancements made to it could be of interest to the current users. Second, an introduction of the idea realized in this setting is transparent and convincing without too much unnecessary notational and algorithmic cluttering. User's information needs may dynamically vary during web searching; often users are not quite sure what we are looking for, in this case the interaction with the search engines cannot be reduced to a simple situation like ''give me a query and here's the answer''. The ''true'' information-based results is in many cases encapsulated inside the overall web searching process, when the users learn more about what they were looking for so to better describe the query phase. The cognitive situations can drastically vary, so it is very difficult to develop a universally better searching paradigm. The need to further enrich the user interaction during web navigation is characterizing the design of recent web-based systems. This work follows this trend, focusing on the impact of user's feedback on Web page categories. Thanks to P-FCM approach the users does not maintain a passive role in that they receive, but they can contribute to add useful, personal information to categorization of Web pages. Ultimately, the challenge is that it possible to have so many options to take into account around web searching activities that both the user and the search engines can get really overwhelmed of a cognitive and operative overhead. We believe that this difficulty can be better faced using agent-based technology. An agent is a persistent software entity, free from the tyranny of Procedure call, as opposite of a traditional program (an agent activation may be independent from an external stimulus). Thanks to the agent-based design, human-computer interaction is moving from direct manipulation towards indirect manipulation. Self-aware agents skilled for web searching can be extremely useful to support users in finding useful information.. Concerning the implementation issue, as the proximity levels between selected Web pages are determined (assessed) by the user, it is not very likely that the technique could be fully automated. Likewise the choice of the pages is done by the user and cannot be replaced. The proposed approach definitely calls for an expanded user interface so that it could facilitate the communication but this is the next development phase not discussed in this paper. Overall, a useful liaison can be create to facilitate an operational control flow between an user profiling component and the P-FCM algorithm. The conceptual models of the user are designed to acquire information about user's behavior so, in case of the existence of such link, we just need to forward part of the acquired information to the P-FCM and then trigger the clustering procedure. This is one of our on-going research activity.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O