Ning-Han Liu

Music Classification Using Significant Repeating Patterns

With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this paper, we propose a new approach for classifying music data based on their contents. In this approach, we focus on monophonic music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to. We perform a series of experiments and the results show that our approach performs on average better than the approach based on the probability distribution of contextual information in music.

Improving Driver Alertness through Music Selection Using a Mobile EEG to Detect Brainwaves

Driving safety has become a global topic of discussion with the recent development of the Smart Car concept. Many of the current car safety monitoring systems are based on image discrimination techniques, such as sensing the vehicle drifting from the main road, or changes in the drivers facial expressions. However, these techniques are either too simplistic or have a low success rate as image processing is easily affected by external factors, such as weather and illumination. We developed a drowsiness detection mechanism based on an electroencephalogram (EEG) reading collected from the driver with an off-the-shelf mobile sensor. This sensor employs wireless transmission technology and is suitable for wear by the driver of a vehicle. The following classification techniques were incorporated: Artificial Neural Networks, Support Vector Machine, and k Nearest Neighbor. These classifiers were integrated with integration functions after a genetic algorithm was first used to adjust the weighting for each classifier in the integration function. In addition, since past studies have shown effects of music on a persons state-of-mind, we propose a personalized music recommendation mechanism as a part of our system. Through the in-car stereo system, this music recommendation mechanism can help prevent a driver from becoming drowsy due to monotonous road conditions. Experimental results demonstrate the effectiveness of our proposed drowsiness detection method to determine a drivers state of mind, and the music recommendation system is therefore able to reduce drowsiness.

Comparison of content-based music recommendation using different distance estimation methods

With rapid growth in the online music market, music recommendation has become an active research area. In most current approaches, content-based recommendation methods play an important role. Estimation of similarity between music content is the key to these approaches. A distance formula is used to calculate the music distance measure, and music recommendations are provided based on this measure. However, people have their own unique tastes in music. This paper proposes a method to calculate a personalized distance measure between different pieces of music based on user preferences. These methods utilize a randomized algorithm, a genetic algorithm, and genetic programming. The first two methods are based on Euclidean distance calculation, where the weight of each music feature in the distance calculation approximates user perception. The third method is not limited to Euclidean distance calculation. It generates a more complex distance function to estimate a user’s music preferences. Experiments were conducted to compare the distance functions calculated by the three methods, and to compare and evaluate their performance in music recommendation.

GeneAlign: a coding exon prediction tool based on phylogenetical comparisons

GeneAlign is a coding exon prediction tool for predicting protein coding genes by measuring the homologies between a sequence of a genome and related sequences, which have been annotated, of other genomes. Identifying protein coding genes is one of most important tasks in newly sequenced genomes. With increasing numbers of gene annotations verified by experiments, it is feasible to identify genes in the newly sequenced genomes by comparing to annotated genes of phylogenetically close organisms. GeneAlign applies CORAL, a heuristic linear time alignment tool, to determine if regions flanked by the candidate signals (initiation codon-GT, AG-GT and AG-STOP codon) are similar to annotated coding exons. Employing the conservation of gene structures and sequence homologies between protein coding regions increases the prediction accuracy. GeneAlign was tested on Projector dataset of 491 human–mouse homologous sequence pairs. At the gene level, both the average sensitivity and the average specificity of GeneAlign are 81%, and they are larger than 96% at the exon level. The rates of missing exons and wrong exons are smaller than 1%. GeneAlign is a free tool available at .

Efficient K-NN search in polyphonic music databases using a lower bounding mechanism

Querying polyphonic music from a large data collection is an interesting and challenging topic. Recently, researchers attempt to provide efficient techniques for content-based retrieval in polyphonic music databases where queries can also be polyphonic. However, most of the techniques do not perform the approximate matching well. In this paper, we present a novel method to efficiently retrieve k music works that contain segments most similar to the user query based on the edit distance. A list-based index structure is first constructed using the feature of the polyphony. A set of candidate approximate answers is then generated for the user query. A lower bounding mechanism is proposed to prune these candidates such that the k answers can be obtained efficiently. The efficiency of the proposed method is evaluated by real data set and synthetic data set, reporting significant improvement over existing approaches in the response time yielded.

PHD: an efficient data clustering scheme using partition space technique for knowledge discovery in large databases

Rapid technological advances imply that the amount of data stored in databases is rising very fast. However, data mining can discover helpful implicit information in large databases. How to detect the implicit and useful information with lower time cost, high correctness, high noise filtering rate and fit for large databases is of priority concern in data mining, specifying why considerable clustering schemes have been proposed in recent decades. This investigation presents a new data clustering approach called PHD, which is an enhanced version of KIDBSCAN. PHD is a hybrid density-based algorithm, which partitions the data set by K-means, and then clusters the resulting partitions with IDBSCAN. Finally, the closest pairs of clusters are merged until the natural number of clusters of data set is reached. Experimental results reveal that the proposed algorithm can perform the entire clustering, and efficiently reduce the run-time cost. They also indicate that the proposed new clustering algorithm conducts better than several existing well-known schemes such as the K-means, DBSCAN, IDBSCAN and KIDBSCAN algorithms. Consequently, the proposed PHD algorithm is efficient and effective for data clustering in large databases.

An intelligent music playlist generator based on the time parameter with artificial neural networks

A music hobbyist listens to different types of music at different times of the day. Thus, an automatic music playlist generator that can adjust to the hobbyists daily activities on this basis is necessary in order to generate the appropriate music to suit the users current activity, whether it is working or driving. Although existing research has introduced various music playlist generators, there is yet a system that generates the music playlist based on time. Hence, in this paper, we present a music playlist generation system, which provides an automatic and personalized music playing service based on the time parameter. This system represents the characteristics of music from features extracted out of both the musics symbolic form and wave data. The kernel of this system is based on a modified artificial neural network. The users music rating history and the associated time stamps in the users profile constitute the training data of the modified artificial neural networks. A collaborative method has also been proposed to reduce the effect of the cold start problem upon system initialization. A series of experiments have been carried out to demonstrate the performance of this system.

An efficient approach to extracting approximate repeating patterns in music databases

Pattern extraction from music strings is an important problem. The patterns extracted from music strings can be used as features for music retrieval or analysis. Previous works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. The concept of the prototypical melody has therefore been proposed to represent these similar music segments. In musicology, the number of music segments that are similar to a prototypical melody implies the importance degree of the prototypical melody to the music work. In this paper, a novel approach is developed to extract all the prototypical melodies in a music work. Our approach considers each music segment as a candidate for the prototypical melody and uses the edit distance to determine the set of music segments that are similar to this candidate. A lower bounding mechanism, which estimates the number of similar music segments for each candidate and prunes the impossible candidates is designed to speed up the process. Experiments are performed on a real data set and the results show a significant improvement of our approach over the existing approaches in the average response time.

Long-Term Animal Observation by Wireless Sensor Networks with Sound Recognition

Due to wireless sensor networks can transmit data wirelessly and can be disposed easily, they are used in the wild to monitor the change of environment. However, the lifetime of sensor is limited by the battery, especially when the monitored data type is audio, the lifetime is very short due to a huge amount of data transmission. By intuition, sensor mote analyzes the sensed data and decides not to deliver them to server that can reduce the expense of energy. Nevertheless, the ability of sensor mote is not powerful enough to work on complicated methods. Therefore, it is an urgent issue to design a method to keep analyzing speed and accuracy under the restricted memory and processor. This research proposed an embedded audio processing module in the sensor mote to extract and analyze audio features in advance. Then, through the estimation of likelihood of observed animal sound by the frequencies distribution, only the interesting audio data are sent back to server. The prototype of WSN system is built and examined in the wild to observe frogs. According to the results of experiments, the energy consumed by sensors through our method can be reduced effectively to prolong the observing time of animal detecting sensors.

Efficient kNN search in polyphonic music databases using a lower bounding mechanism

AbstractQuerying polyphonic music from a large data collection is an interesting topic. Recently, researchers have attempted to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this paper, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion and/or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operations to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases. The experimental results show that our method outperforms the previous methods in efficiency. We also evaluate the effectiveness of our method by showing the search results to the musician and nonmusician user groups. The experimental results provide useful guidelines on the design of a polyphonic music database.