Performing Local Identity in a Contemporary Urban Society: A Study of Ping-tan Narrative Vocal Tradition in Suzhou, China

China has many rich traditions of storytelling and story singing, which are deeply rooted oral traditions in their particular geographical areas, carrying the linguistic and cultural flavours of their localities. In Suzhou, the central city of the Yangtze Delta’s Wu area, the storytelling genre pinghua and the story singing genre tanci have become emblematic of regional identity. Since the 1950s, the two genres have been referred to under the hybrid generic name ‘Suzhou ping-tan’ after the city, or simply ping-tan in abbreviation. Nowadays typically comprising extended narratives performed over the course of half a month, ping-tan has maintained popularity up to the present day. Each afternoon, people go to the unique performance venue of the shuchang (‘story house’), which combines teahouse, performance venue and social centre, to enjoy solo or duet performances given by shuoshu xiansheng (‘storytellers’). The sung episodes are set to an accompaniment of sanxian banjo and – in duet performance – also pipa lute. In the context of face-to-face communication, establishing an empathetic bridge between storyteller and audience is of paramount importance, necessitating storytellers to polish and tailor their artistry efficiently in response to audience feedback. Following the development of radio broadcasting since the 1920s and television since the 1980s, ping-tan has also been widely delivered directly into people’s homes. Listening to and watching ping-tan has become a part of many local people’s daily habits. This thesis seeks to explain how Suzhou ping-tan has maintained its vitality in contemporary society. Various oral performance traditions have declined with the range of alternative types of entertainment that have bloomed in recent times, yet a great many Suzhou citizens still take for granted that ping-tan represents their local cultural identity. Drawing upon fieldwork conducted since 2011, this thesis explores the interconnectedness between the storyteller and audiences during and outside of performance. It analyses performer/audience ‘feed-back loop’ communication within a variety of fields of ping-tan activity, focusing in particular on the following areas: the role-playing and identity presentation of storytellers and audience members, the different types of ping-tan follower and their respective forms of involvement, the use of gesture in performance to communicate further layers of meaning, the nature of the mutually complementary relationship between words and music in ballad singing, and the effects of television and radio dissemination on ping-tan culture. This thesis identifies ‘feed-back loop’ interplay as being a key factor in ping-tan’s success, facilitating the multi-faceted involvement of all participants within a flexible and unpredictable shared experience

Automatic recognition of Persian musical modes in audio musical signals

This research proposes new approaches for computational identification of Persian musical modes. This involves constructing a database of audio musical files and developing computer algorithms to perform a musical analysis of the samples. Essential features, the spectral average, chroma, and pitch histograms, and the use of symbolic data, are discussed and compared. A tonic detection algorithm is developed to align the feature vectors and to make the mode recognition methods independent of changes in tonality. Subsequently, a geometric distance measure, such as the Manhattan distance, which is preferred, and cross correlation, or a machine learning method (the Gaussian Mixture Models), is used to gauge similarity between a signal and a set of templates that are constructed in the training phase, in which data-driven patterns are made for each dastgàh (Persian mode). The effects of the following parameters are considered and assessed: the amount of training data; the parts of the frequency range to be used for training; down sampling; tone resolution (12-TET, 24-TET, 48-TET and 53-TET); the effect of using overlapping or nonoverlapping frames; and silence and high-energy suppression in pre-processing. The santur (hammered string instrument), which is extensively used in the musical database samples, is described and its physical properties are characterised; the pitch and harmonic deviations characteristic of it are measured; and the inharmonicity factor of the instrument is calculated for the first time. The results are applicable to Persian music and to other closely related musical traditions of the Mediterranean and the Near East. This approach enables content-based analyses of, and content-based searches of, musical archives. Potential applications of this research include: music information retrieval, audio snippet (thumbnailing), music archiving and access to archival content, audio compression and coding, associating of images with audio content, music transcription, music synthesis, music editors, music instruction, automatic music accompaniment, and setting new standards and symbols for musical notation

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference

The Effect of Code Obfuscation on Authorship Attribution of Binary Computer Files

In many forensic investigations, questions linger regarding the identity of the authors of the software specimen. Research has identified methods for the attribution of binary files that have not been obfuscated, but a significant percentage of malicious software has been obfuscated in an effort to hide both the details of its origin and its true intent. Little research has been done around analyzing obfuscated code for attribution. In part, the reason for this gap in the research is that deobfuscation of an unknown program is a challenging task. Further, the additional transformation of the executable file introduced by the obfuscator modifies or removes features from the original executable that would have been used in the author attribution process. Existing research has demonstrated good success in attributing the authorship of an executable file of unknown provenance using methods based on static analysis of the specimen file. With the addition of file obfuscation, static analysis of files becomes difficult, time consuming, and in some cases, may lead to inaccurate findings. This paper presents a novel process for authorship attribution using dynamic analysis methods. A software emulated system was fully instrumented to become a test harness for a specimen of unknown provenance, allowing for supervised control, monitoring, and trace data collection during execution. This trace data was used as input into a supervised machine learning algorithm trained to identify stylometric differences in the specimen under test and provide predictions on who wrote the specimen. The specimen files were also analyzed for authorship using static analysis methods to compare prediction accuracies with prediction accuracies gathered from this new, dynamic analysis based method. Experiments indicate that this new method can provide better accuracy of author attribution for files of unknown provenance, especially in the case where the specimen file has been obfuscated

Query by Example of Speaker Audio Signals using Power Spectrum and MFCCs

Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%

Audio Content-Based Music Retrieval

The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework