A Highly Robust Audio Monitoring System for Radio Broadcasting

Proposing a novel approach for monitoringsongs for the radio broadcasting channels is veryimportant for the interest of singers, writers andmusicians in the musical industry. Singers, writers andmusicians have a claim to intellectual property rightsfor their songs broadcast over all the radio channels.According to this intellectual property rights actsingers, writers and musicians should be paid for theirsongs broadcast over all the radio channels. Therefore wepropose a real time audio monitoring approach to solvethis problem which includes our own audio recognitionalgorithm. It is easy to recognize a song, when you providethe original high quality blueprint of the song as input. Butwe can’t expect such kind of audio input from radiochannels since lots of transformations are possible beforereaching the end user or listener. For example, addingenvironmental effects such as noise, adding commercialson the song as watermarks, playing more than one songas a chain without adding any silence between them,playing a part of the song, playing same song in variousspeeds and so on. These transformations cause change inthe uniqueness of particular song and make the problemeven more difficult. The algorithm we proposing is resistantto noise and distortion as well as it is capable of recognizingshort segment of song when broadcasting over the radiochannels. At the end of the processing our system generatesa descriptive report including title of the song, singer of thesong, writer of the song, composer of the song, number oftimes it was played and when it was played for all songs fora particular period for all radio broadcasting channels. Weevaluate our system against various types of real timescenarios and achieved overall higher level of accuracy(96%) at the end

A Novel Framework for Efficient Automated Singer Identification in Large Music Databases

10.1145/1508850.1508856ACM Transactions on Information Systems273ATIS

Music CD in development and consumer value in the Thai music industry

While the digital market, especially the music streaming market, has rapidly grown in recent years, however the physical music segment still remains relevant in the Thai market. The Thai music market has inimitable characteristics within the market in terms of the physical music record offers, recorded musical works, and a growth trend in physical sales. Moreover, the behaviour in physical music consumption is opposite to that in world markets. Music consumption practices in the Thai market and why physical music, and CDs in particular, remain relevant to the Thai music industry are an enigma. The music industry itself has suggested that the physical music market needs to be revamped and its physical products redeveloped. In addition, the major record companies have also refocused into developing physical markets. However, precisely how this is to be achieved has not been specified. The twin aims of this study are to more fully comprehend Thai music consumption practices in today’s market and to examine how the concepts of product development could be effective in responding to consumer needs and desires. Consumer-led product development is the main concept of this study used to create ideas to enhance music CDs. This study combined many perspectives related to consumer-led product development and then applied them to construct the conceptual framework named “The Seminal Framework for CD Development”. The framework is a roadmap to create a new set of features for a new form of music CD based on the input of the music industry’s representatives and consumers. A new form of music CD which includes a new set of features is named in this study as the “prototype CD”. Also, the framework is used to evaluate the effectiveness of the prototype CD; how the prototype CD is responsive to consumer needs as far as functional and psychological perspectives are concerned. Bearing in mind the aim of this research, the researcher considered the interpretive paradigm to be the most appropriate approach for capturing consumers’ experiences in music consumption practices and for studying the opinions, points of view and ideas derived from the consumers, and the experts in music CD development. In the data collection process, this study employed the technique of purposive sampling for selecting from the population. The purposive sampling technique allows the researcher to judge and select people or prospective participants who: 1) are available to participate I in conducting the research, 2) are knowledgeable about the industry, 3) have experience related to the context of the study, and 4) can provide the reliable and detailed information required to understand the focal themes of the study. This study conducted nine interviews with the music industry’s experts, 60 one-on-one interviews and four group interviews with consumers. For the data analysis, this study adopted the manual coding analysis. The Seminal Framework determined the coding structure, and sets of data could be organised into distinct themes, such as the new features of music CDs or future positive possibilities for music consumption. This enabled, at the end of the process, an easier and more efficient identification of the experiential values derived from prototype music CDs. In addition, in more fully understanding the needs and expectations inherent in music consumption practices, such careful coding analysis helps to re-define the typology of music consumers. The typology and the concepts also facilitated the identification of music consumption behaviour in today’s environment. This study contributes a wider concept in consumer-led product development that has been applied to the context of music consumption practices and music product (CD) development

Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics

This paper presents an effective technique for automatically clustering undocumented music recordings based on their associated singer. This serves as an indispensable step towards indexing and content-based information retrieval of music by singer. The proposed clustering system operates in an unsupervised manner, in which no prior information is available regarding the characteristics of singer voices, nor the population of singers. Methods are presented to separate vocal from non-vocal regions, to isolate the singers' vocal characteristics from the background music, to compare the similarity between singers' voices, and to determine the total number of unique singers from a collection of songs. Experimental evaluations conducted on a 200-track pop music database confirm the validity of the proposed system

A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features

Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity. Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations. In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended

Statistical Approaches for Signal Processing with Application to Automatic Singer Identification

In the music world, the oldest instrument is known as the singing voice that plays an important role in musical recordings. The singer\u27s identity serves as a primary aid for people to organize, browse, and retrieve music recordings. In this thesis, we focus on the problem of singer identification based on the acoustic features of singing voice. An automatic singer identification system is constructed and has achieved a very high identification accuracy. This system consists of three crucial parts: singing voice detection, background music removal and pattern recognition. These parts are introduced and explored in great details in this thesis. To be specific, in terms of the singing voice detection, we firstly study a traditional method, double GMM. Then an improved method, namely single GMM, is proposed. The experimental result shows that the detection accuracy of single GMM can be achieved as high as 96.42%. In terms of the background music removal, Non-negative Matrix Factorization (NMF) and Robust Principal Component Analysis (RPCA) are demonstrated. The evaluation result shows that RPCA outperforms NMF. In terms of pattern recognition, we explore the algorithms of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). Based on the experimental results, it turns out that the prediction accuracy of GMM classifier is about 16% higher than SVM

Singing Voice Recognition for Music Information Retrieval

This thesis proposes signal processing methods for analysis of singing voice audio signals, with the objectives of obtaining information about the identity and lyrics content of the singing. Two main topics are presented, singer identification in monophonic and polyphonic music, and lyrics transcription and alignment. The information automatically extracted from the singing voice is meant to be used for applications such as music classification, sorting and organizing music databases, music information retrieval, etc. For singer identification, the thesis introduces methods from general audio classification and specific methods for dealing with the presence of accompaniment. The emphasis is on singer identification in polyphonic audio, where the singing voice is present along with musical accompaniment. The presence of instruments is detrimental to voice identification performance, and eliminating the effect of instrumental accompaniment is an important aspect of the problem. The study of singer identification is centered around the degradation of classification performance in presence of instruments, and separation of the vocal line for improving performance. For the study, monophonic singing was mixed with instrumental accompaniment at different signal-to-noise (singing-to-accompaniment) ratios and the classification process was performed on the polyphonic mixture and on the vocal line separated from the polyphonic mixture. The method for classification including the step for separating the vocals is improving significantly the performance compared to classification of the polyphonic mixtures, but not close to the performance in classifying the monophonic singing itself. Nevertheless, the results show that classification of singing voices can be done robustly in polyphonic music when using source separation. In the problem of lyrics transcription, the thesis introduces the general speech recognition framework and various adjustments that can be done before applying the methods on singing voice. The variability of phonation in singing poses a significant challenge to the speech recognition approach. The thesis proposes using phoneme models trained on speech data and adapted to singing voice characteristics for the recognition of phonemes and words from a singing voice signal. Language models and adaptation techniques are an important aspect of the recognition process. There are two different ways of recognizing the phonemes in the audio: one is alignment, when the true transcription is known and the phonemes have to be located, other one is recognition, when both transcription and location of phonemes have to be found. The alignment is, obviously, a simplified form of the recognition task. Alignment of textual lyrics to music audio is performed by aligning the phonetic transcription of the lyrics with the vocal line separated from the polyphonic mixture, using a collection of commercial songs. The word recognition is tested for transcription of lyrics from monophonic singing. The performance of the proposed system for automatic alignment of lyrics and audio is sufficient for facilitating applications such as automatic karaoke annotation or song browsing. The word recognition accuracy of the lyrics transcription from singing is quite low, but it is shown to be useful in a query-by-singing application, for performing a textual search based on the words recognized from the query. When some key words in the query are recognized, the song can be reliably identified

Reimagining the blues: A new narrative for 21st century blues music

This project explores the extent to which blues music in the 21st century is linked to its cultural past through identification and examination of the key concepts and relationships that may contribute to a contemporary understanding of the blues and cultural artefacts, as circulated and consumed in popular music practices. Despite the vast amount of scholarship on blues music, including revisionist literature that emerged in the late 20th century and in the first decade of this century, there has been no singular study of popular music or the blues that has specifically addressed the sociocultural and musicological links between the traditions of the past in the context of 21st century popular music in sufficient depth and so research into contemporary interpretations of blues music as it exists in the 21st century remains relatively scarce. This project provides an account of the cultural resonances and development of the blues genre in popular music culture to establish what the blues means, how it means, and to who it is meaningful through the formulation of a conceptual framework offered as a unique methodological tool for identifying and exploring blues music in the 21st century. Within this interdisciplinary framework, concepts including those concerned with technological mediation, intertextuality, cultural identity, memory, and meaning, are mobilised, refined, and combined in order to reveal and explore problematic relationships that exist in and between concepts of race, place, and technology as connected to blues music in the 21st century. Through an ethnomusicological strategy of enquiry and largely inductive approach to the collection of qualitative and quantitative data, the results of analyses conducted using a broad range of methods including music theoretic analysis, semiotics, intertextuality, survey, and interview are presented in order to both address how and why a contemporary blues music revival may be perceived to be taking place and to offer a fresh historical review of the context in which the blues has developed from a 21st century platform. This study finds that popular music performers and consumers are continually reimagining the blues through engagement with the traditions of the past and accordingly argues for an extension to the boundaries of blues music in its stylistic and cultural categorisation in 21st-century discourse. It is also argued that the results of research presented here also go some way in illustrating both how such engagement with the traditions of the past may directly reflect tensions in contemporary society, and how blues-marketed artefacts are demarcated and declassified within the music industry.N/