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In this paper, we introduce a synchronization method of music and video, where the music is arranged with the video using emotion similarity. The music segments are matched with the video segments so that the music best follows the video's narrative. The temporal order of video segments are unchanged. Due to the disparity of structural musical content between segment boundaries, we design a cost function...
The amount of audio data on public networks like Internet is increasing in huge volume daily. So to access these media, we need to efficiently index and annotate them. Due to non-stationary nature and discontinuities present in the audio signal, segmentation and classification of audio signal has really become a challenging task. Automatic music classification and annotation is also one of the challenging...
Identification of musical instruments from the acoustic signal using speech signal processing methods is a challenging problem. Further, whether this identification can be carried out by a single musical note, like humans are able to do, is an interesting research issue that has several potential applications in the music industry. Attempts have been made earlier using the spectral and temporal features...
In this paper, we present an automated music video generation framework that utilizes emotion synchronization between video and music. After a user uploads a video or music, the framework segments the video and music, and then predicts the emotion of each of the segments. The preprocessing result is stored on the server's database. The user can select a set of videos and music from the database, and...
This research task looks at the part lyric content and additionally audio features can play in enhancing audio music mood arrangement. With the developing measure of computerized music and human different requests to music data recovery, programmed music conclusion investigation is turning into a critical and vital assignment for different framework and applications, for example, music association,...
This paper focuses on cover song identification over a large-scale dataset. Identifying all covers of a query song from music collection is a challenging task since covers vary in multiple aspects, such as tempo, key, and structure. For the large-scale dataset, cover song identification is more challenging and few works have been published. Previous works usually use a single representation for a...
Usually, music is generally classified on the basis of its genre which indicates its musical style or musical form based on some sort of shared history. On the contrary, this paper aims to classify a given track into a mood such as happy, sad, peaceful and angry rather than based on its genre because more often than not, the listener prefers to hear songs similar to each other both in terms of the...
In this paper, we present an online handwritten system for music score recognition. Music score is used to record a music song. People often used to compose a music score on the sheet of paper. In our system, we propose the pen based writing method and use multi-strokes to form a music notation. We extract the height, shape and direction from a stroke as the features and recognize it as a symbol....
With the popularization of mobile devices and cloud storages, people tend to store photos and videos through internet. Recently many cloud systems start providing functions to produce reviewing films from stored media files. However, the background music of the film usually does not match the video's emotion because the added music is usually selected randomly. This work proposed an emotion-based...
Music information retrieval (MIR) is one of the vast areas of research and it is gaining more and more attention from researchers, as well as from the music developing community. Music can be classified throughout many dimensions such as genre, mood, instrument, artists, etc. Emotion based (Mood-based) music classification is also carried out by researchers for understanding Physiological and Psychological...
This work explores the use of Empirical Mode Decomposition (EMD) for discriminating speech regions from music in audio recordings. The different frequency scales or Intrinsic Mode Functions (IMFs) obtained from EMD of the audio signal are found to contain discriminatory evidence for distinguishing the speech regions from the music regions of the audio signal. Different statistical measures like mean,...
Recently, advanced multimedia technologies enable a rapid growth of music data. It is accordingly a challenging issue to effectively retrieve the desired music pieces from a music collection. Traditional solutions for music retrieval can be divided into two types, namely text-based music retrieval and content-based music retrieval. However, it is difficult to satisfy both textual-percept and audio-content...
Music data analysis and retrieval has become a vital and challenging research field due to the increasing need to manage large amount of musical data present on multimedia. Music analysis deals with extracting various features, which describe the music signal, and use those features to process the music samples for various applications such as indexing, retrieval, automatic classification, etc. This...
Speech is structured acoustic signals which form a message featuring the speaker's language, speaking style, and also underlying emotion. These features affect the information passed through speech. Automated speech emotion recognition is a field long studied, but it has not yet found a quite reliable approach. This paper explores two ways to improve automated recognition. First, new sets or combinations...
Current query by humming system can hardly be extended to large massive database as most of them adopt the features extracted from MIDI files which are not widely used and the very time-consuming match methods. In this work, we regard query by humming as a subsequence similarity match problem and exploit the modified SPRING algorithm instead of DTW as the core match method to compare the melody feature...
This paper presents a dynamic ensemble selection method for music genre classification which employs two pools of diverse classifiers. The pools of classifiers are created by using different features types extracted from three distinct segments of each music piece. From these initial pools of weak classifiers, ensembles of classifiers are dynamically selected for each test pattern using the k-nearest...
This paper investigates a given branch of the music information retrieval field, specifically that of audio thumbnail, through which a piece of music can be automatically summarized. We first provide a survey on the different techniques available today for the generation of music summaries, and then we propose a practical method that, based on best practices, is able to summarize songs with a great...
Music Information Retrieval (MIR) is an interesting area of investigation. The MIR research aims to develop new techniques for processing musical information and searching music databases by content. Therefore, robust retrieval and matching techniques are required. This paper devises a more practical and efficient approach to MIR by investigating a variety of statistical and signal processing-based...
This paper proposes a musical instrument identification, melody and bass line estimation method of mixture sound signal for automatic music transcription. The method is based on sound feature verification using musical sound feature database such as tone (timbre) and pitch database. First, musical instrument is identified using MFCC. Then, melody and bass lines are estimated using time-frequency power...
Advancement in computing has opened up the possibility of mechanizing the process of music creation. Through Concatenative Sound Synthesis, best matched segments between target and source sounds are found and synthesized. Factors that affect the distance of the match, such as the order and weight of the features are examined and presented here. A robust algorithm to automatically assign consistent...
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