We present a graph-based engine for computing chord tone soloing suggestions for guitar students. Chord tone soloing is a fundamental practice for improvising over a chord progression, where the instrumentalist uses only the notes contained in the current chord. This practice is a building block for all advanced jazz guitar theory but is difficult to learn and practice. First, we discuss methods for generating chord-tone arpeggios. Next, we construct a weighted graph where each node represents a chord tone arpeggio for a chord in the progression. Then, we calculate the edge weight between each consecutive chord's nodes in terms of optimal transition tones. We then find the shortest path through this graph and reconstruct a chord-tone soloing line. Finally, we discuss a user-friendly system to handle input and output to this engine for guitar students to practice chord tone soloing.

Automatic transcription of guitar strumming is an underrepresented and challenging task in Music Information Retrieval (MIR), particularly for extracting both strumming directions and chord progressions from audio signals. While existing methods show promise, their effectiveness is often hindered by limited datasets. In this work, we extend a multimodal approach to guitar strumming transcription by introducing a novel dataset and a deep learning-based transcription model. We collect 90 min of real-world guitar recordings using an ESP32 smartwatch motion sensor and a structured recording protocol, complemented by a synthetic dataset of 4h of labeled strumming audio. A Convolutional Recurrent Neural Network (CRNN) model is trained to detect strumming events, classify their direction, and identify the corresponding chords using only microphone audio. Our evaluation demonstrates significant improvements over baseline onset detection algorithms, with a hybrid method combining synthetic and real-world data achieving the highest accuracy for both strumming action detection and chord classification. These results highlight the potential of deep learning for robust guitar strumming transcription and open new avenues for automatic rhythm guitar analysis.

Guitar tablatures enrich the structure of traditional music notation by assigning each note to a string and fret of a guitar in a particular tuning, indicating precisely where to play the note on the instrument. The problem of generating tablature from a symbolic music representation involves inferring this string and fret assignment per note across an entire composition or performance. On the guitar, multiple string-fret assignments are possible for most pitches, which leads to a large combinatorial space that prevents exhaustive search approaches. Most modern methods use constraint-based dynamic programming to minimize some cost function (e.g.\ hand position movement). In this work, we introduce a novel deep learning solution to symbolic guitar tablature estimation. We train an encoder-decoder Transformer model in a masked language modeling paradigm to assign notes to strings. The model is first pre-trained on DadaGP, a dataset of over 25K tablatures, and then fine-tuned on a curated set of professionally transcribed guitar performances. Given the subjective nature of assessing tablature quality, we conduct a user study amongst guitarists, wherein we ask participants to rate the playability of multiple versions of tablature for the same four-bar excerpt. The results indicate our system significantly outperforms competing algorithms.

Tablature notation is widely used in popular music to transcribe and share guitar musical content. As a complement to standard score notation, tablatures transcribe performance gesture information including finger positions and a variety of guitar-specific playing techniques such as slides, hammer-on/pull-off or bends.This paper focuses on bends, which enable to progressively shift the pitch of a note, therefore circumventing physical limitations of the discrete fretted fingerboard. In this paper, we propose a set of 25 high-level features, computed for each note of the tablature, to study how bend occurrences can be predicted from their past and future short-term context. Experiments are performed on a corpus of 932 lead guitar tablatures of popular music and show that a decision tree successfully predicts bend occurrences with an F1 score of 0.71 anda limited amount of false positive predictions, demonstrating promising applications to assist the arrangement of non-guitar music into guitar tablatures.

The legendary guitarist Eddie Van Halen is regarded by many as one of the best of all time. Numerous musicians have been inspired by his virtuosic playing style and cutting-edge approaches, which have had a long-lasting effect on the music business. Do you know about his remarkable corpus of work? Eddie has a genuinely outstanding resume, which includes his early years with the band Van Halen as well as his solo endeavors and group efforts. Think about his time with Van Halen.

Fox News Flash top entertainment and celebrity headlines are here. Check out what clicked this week in entertainment. Tom Morello, lead guitarist of Los Angeles rock band Rage Against the Machinne, was accidentally tackled by a security guard who was chasing a fan that rushed the stage during a Toronto concert Saturday night. Morello and the band were playing their final song, "Killing in the Name," when a fan in a red shirt jumped onto the stage, according to video posted online. In the video, a security guard can be seen chasing after the fan, but accidentally tackles Morello -- who falls off the stage -- as the fan jumps down and tries to escape back into the crowd.

It may come as little surprise to learn that musicians perceive sounds differently than the average person. But according to a new study, there are striking differences among the artists themselves, too. MRI scans on the brains of guitarists and beatboxers have revealed that their responses to hearing unfamiliar songs differ based on their craft; while the'hand area' of a guitarist's brain lights up, it's the'mouth area' that activates for beatboxers. For beatboxers, the music spurred activity in the region that controls mouth movements. For guitarists, hearing a guitar track led to activity in an area responsible for hand movement.

Eight years ago, when neurosurgeon Marcelo Galarza saw images from jazz guitarist Pat Martino's cerebral MRI, he was astonished. "I couldn't believe how much of his left temporal lobe had been removed," he said. Martino had brain surgery in 1980 to remove a tangle of malformed veins and arteries. At the time he was one of the most celebrated guitarists in jazz. Yet few people knew that Martino suffered epileptic seizures, crushing headaches, and depression. Locked in psychiatric wards, he withstood debilitating electroshock therapy. It wasn't until 2007 that Martino had an MRI and not until recently that neuroscientists published their analyses of the images.

Digital music service Spotify recently acquired machine learning startup Niland to improve its recommendation and personalization technologies. In other words, Spotify wants to better connect its users to music they will like. The heart of this concept is nothing new. Netflix and Amazon, too, use machine learning – a type of artificial intelligence (AI) in which machines learn when exposed to new data without being programmed – to suggest content and products their respective users might like. And while this ability to tap into AI – machines that perform smart, human-like tasks – to analyze internal data is increasingly common, it's a bit more complicated when it comes to using AI to capture consumer attention externally, like, say, in search.

Listen to this three-piece with your eyes closed and it could be any group of musicians plucking a guitar, twinkling on an electric keyboard, or beating a drum. Sure, there's a synthesized quality to the music -- which sweeps from orchestral to experimental rock -- but what band doesn't get a little help from computers these days? Open your eyes and you'll find something very different indeed. For starters, the guitarist is a humanoid looming two-meters-tall, with 78 fingers sweeping across the glowing instrument strapped to its torso. The rocking robot -- called March -- bangs its impressive mane of multi-colored cables in time to the music, albeit a little jerkily.