Can Current Stereo Recording Techniques Improve? A Creative Analysis and Experiment on Stereo Recording

Listening to music in stereo has become the societal norm and because of its popularity, it has become, “invisible by its own success” (Théberge 1). Contrary to its growth, the older and more commonly used stereo microphone techniques are still being used today and very few new and unique techniques have made a similar impact. To test if the commonly used techniques have room for improvement this project compares them to brand-new ones. In order to understand and utilize stereo microphone placements numerous fundamental ideas need to be considered to achieve a certain sound and level of quality. Every microphone technique variation is used for a certain reason based on how it changes the stereo image, phasing, localization, practicality, and creative manipulation abilities. The purpose of this project is to learn about the evolution and art form of stereo microphone techniques and then analyze the information to create new and unique stereo microphone techniques to compare against the common techniques indicating if they could be improved or not

Classification of musical genres using hidden Markov models

The music content online is expanding fast, and music streaming services are in need for algorithms that sort new music. Sorting music by their characteristics often comes down to considering the genre of the music. Numerous studies have been made on automatic classification of audio files using spectral analysis and machine learning methods. However, many of the completed studies have been unrealistic in terms of usefulness in real settings, choosing genres that are very dissimilar. The aim of this master’s thesis is to try a more realistic scenario, with genres of which the border between them is uncertain, such as Pop and R&B. Mel-frequency cepstral coefficients (MFCCs) were extracted from audio files and used as a multidimensional Gaussian input to a hidden Markov model (HMM) to classify the four genres Pop, Jazz, Classical and R&B. An alternative method is tested, using a more theoretical approach of music characteristics to improve classification. The maximum total accuracy obtained when tested on an external test set was 0.742 for audio data, and 0.540 for theoretical data, implying that a combination of the two methods will not result in an increase of accuracy. Different methods of evaluation and possible alternative approaches are discussed

Stereo Panning Features for Classifying Recording Production Style

Recording engineers, mixers and producers play important yet often overlooked roles in defining the sound of a particular record, artist or group. The placement of different sound sources in space using stereo panning information is an important component of the production process. Audio classification systems typically convert stereo signals to mono and to the best of our knowledge have not utilized information related to stereo panning. In this paper we propose a set of audio features that can be used to capture stereo information. These features are shown to provide statistically important information for non-trivial audio classification tasks and are compared with the traditional Mel-Frequency Cepstral Coefficients. The proposed features can be viewed as a first attempt to capture extra-musical information related to the production process through music information retrieval techniques.

Gendering the Virtual Space: Sonic Femininities and Masculinities in Contemporary Top 40 Music

This dissertation analyzes vocal placement—the apparent location of a voice in the virtual space created by a recording—and its relationship to gender. When listening to a piece of recorded music through headphones or stereo speakers, one hears various sound sources as though they were located in a virtual space (Clarke 2013). For instance, a specific vocal performance—once manipulated by various technologies in a recording studio—might evoke a concert hall, an intimate setting, or an otherworldly space. The placement of the voice within this space is one of the central musical parameters through which listeners ascribe cultural meanings to popular music. I develop an original methodology for analyzing vocal placement in recorded popular music. Combining close listening with music information retrieval tools, I precisely locate a voice’s placement in virtual space according to five parameters: (1) Width, (2) Pitch Height, (3) Prominence, (4) Environment, and (5) Layering. I use the methodology to conduct close and distant readings of vocal placement in twenty-first-century Anglo-American popular music. First, an analysis of “Love the Way You Lie” (2010), by Eminem feat. Rihanna, showcases how the methodology can be used to support close readings of individual songs. Through my analysis, I suggest that Rihanna’s wide vocal placement evokes a nexus of conflicting emotions in the wake of domestic violence. Eminem’s narrow placement, conversely, expresses anger, frustration, and violence. Second, I use the analytical methodology to conduct a larger-scale study of vocal placement in a corpus of 113 post-2008 Billboard chart-topping collaborations between two or more artists. By stepping away from close readings of individual songs, I show how gender stereotypes are engineered en masse in the popular music industry. I show that women artists are generally assigned vocal placements that are wider, more layered, and more reverberated than those of men. This vocal placement configuration—exemplified in “Love the Way You Lie”—creates a sonic contrast that presents women’s voices as ornamental and diffuse, and men’s voices as direct and relatable. I argue that these contrasting vocal placements sonically construct a gender binary, exemplifying one of the ways in which dichotomous conceptions of gender are reinforced through the sound of popular music