Assistive Guitar Plucking Device and User Interface

The goal of the project was to develop a wireless, battery-powered device with a plucking mechanism and a wearable user interface (UI) that would enable individuals with physical disabilities to play a guitar. The plucking mechanism, mounted to an adjustable frame around the guitar, has 3D printed plectra to actuate each guitar string. The inertial measurement unit based UI is comprised of two wearable devices that calculate which strings to pluck, by using sensor fusion to track user motion and device orientation. Music provides people an avenue to express their emotions and has been shown to stimulate the brain in uniquely beneficial ways. This project provides an opportunity to create music for individuals who have not previously had access to this experience

Art for the Disabled

Diseases such as ALS and muscular dystrophy have no cure, and cause a loss of muscle function as the disease progresses. This project was aimed to help those with disabled arms or hands play the bass guitar, creating a prototype bass guitar that received user input from an easy to use controller, designed to mimic bass playing in its design. A microcontroller processes the signals from the user and from the guitar, and controls a motor that adjusts the tension on the string

Stringless Guitar

The aim of this project is to improve the design of a typical guitar by designing a digital stringless guitar. Due to the nonlinearities inherent in a guitar, it would be difficult to reproduce guitar tones by summing harmonic components; therefore, digital samples of guitar tones were taken in order to preserve these unique and wonderfully sounding tones. These digital samples were stored in the guitar and used to produce its tones when played. The digital guitar includes fingerboard position sensors as well as strum sensors for each string. The fingerboard position sensors detect the player’s fingers at discrete positions. The strum sensors detect which string is being strummed. All the sensors are durable and may be replaced as the instrument ages. The guitar produces sound similar to a typical guitar without requiring strings or tuning. The stringless guitar has many of the standard features of a typical guitar including a standard guitar audio jack, volume control and tone control. The hope of this project is to produce a novel digital guitar that may be admired by professional and beginner musicians alike

Mechalele (Self-Playing Ukulele)

This project involves the design and implementation of a self-playing ukulele. The ukulele plays chords and plucks open strings based on a song or chord progression selected by the user. Each string on the ukulele has a designated hobby servo motor that plucks the string with pre-programmed timing as is shown in Figure 1 (3). The plucking is achieved by attaching a flexible piece of cardboard to the shaft of the servo to act as the guitar pick. By moving the motors together at once, the action of strumming can be approximated. The design uses the last three frets on the ukulele for pressing different strings and allows the ukulele to play different notes and chords. The strings are pressed by small push/pull solenoids (2) that are spring loaded to push the string when active and not touch the string when passive. The output of the system is controlled with user selection buttons that selects song or tuning mode for the ukulele from an LCD display (6). The user can either select tuning or song playing mode using three pushbuttons. In tuning mode, a speaker plays the desired note for a string and the LCD display prompts the user to pluck that string. The frequency of the note is detected from a microphone and the display tells the user if the string is tuned, too high, or too low

AVISARME: Audio Visual Synchronization Algorithm for a Robotic Musician Ensemble

This thesis presents a beat detection algorithm which combines both audio and visual inputs to synchronize a robotic musician to its human counterpart. Although there has been considerable work done to create sophisticated methods for audio beat detection, the visual aspect of musicianship has been largely ignored. With advancements in image processing techniques, as well as both computer and imaging technologies, it has recently become feasible to integrate visual inputs into beat detection algorithms. Additionally, the proposed method for audio tempo detection also attempts to solve many issues that are present in current algorithms. Current audio-only algorithms have imperfections, whether they are inaccurate, too computationally expensive, or suffer from terrible resolution. Through further experimental testing on both a popular music database and simulated music signals, the proposed algorithm performed statistically better in both accuracy and robustness than the baseline approaches. Furthermore, the proposed approach is extremely efficient, taking only 45ms to compute on a 2.5s signal, and maintains an extremely high temporal resolution of 0.125 BPM. The visual integration also relies on Full Scene Tracking, allowing it to be utilized for live beat detection for practically all musicians and instruments. Numerous optimization techniques have been implemented, such as pyramidal optimization (PO) and clustering techniques which are presented in this thesis. A Temporal Difference Learning approach to sensor fusion and beat synchronization is also proposed and tested thoroughly. This TD learning algorithm implements a novel policy switching criterion which provides a stable, yet quickly reacting estimation of tempo. The proposed algorithm has been implemented and tested on a robotic drummer to verify the validity of the approach. The results from testing are documented in great detail and compared with previously proposed approaches

Magpick: an Augmented Guitar Pick for Nuanced Control

This paper introduces the Magpick, an augmented pick for electric guitar that uses electromagnetic induction to sense the motion of the pick with respect to the permanent magnets in the guitar pickup. The Magpick provides the guitarist with nuanced control of the sound that coexists with traditional plucking-hand technique. The paper presents three ways that the signal from the pick can modulate the guitar sound, followed by a case study of its use in which 11 guitarists tested the Magpick for five days and composed a piece with it. Reflecting on their comments and experiences, we outline the innovative features of this technology from the point of view of performance practice. In particular, compared to other augmentations, the high temporal resolution, low latency, and large dynamic range of the Magpick support a highly nuanced control over the sound. Our discussion highlights the utility of having the locus of augmentation coincide with the locus of interaction

LED Learning Guitar

The goal of this project is to create an electric guitar that detects and displays chords in different tunings for learning and exploration purposes. A hexaphonic pickup will generate the required signals and a processor will detect the pitch. The computer software will apply the pitch information to determine the tuning and the voicings of chords. LEDs mounted on the fretboard of the guitar will indicate a note to be played