Posts

“In C” is a work of Western contemporary music, but it shares key qualities with Buddhist chanting and shōmyō:

Image
 “In C” is a work of Western contemporary music, but it shares key qualities with Buddhist chanting and shōmyō: a layered overtone resonance and a ritual sense of time. The continuous high C pulse and sustained tones create a drone, similar to how steady pitch in sutra chanting turns the entire room into a resonating chamber. Multiple players repeat the same patterns with slight timing differences, producing interference “beating” — a thick, swelling vibration you feel in the body, much like group chanting. The piece is designed not to tell a dramatic story or drive toward a destination, but to sink the listener into the present moment. Its rhythm is collectively breathed rather than mechanically enforced. As a result, “In C” can feel less like a concert and more like shared invocation. Terry Riley's In C: Performance Guidelines Summary. All performers play 53 patterns from the same score in sequence; ensemble size and instrumentation are flexible (ideally around 35 players). Vocal...

YouTube-friendly examples of “vamp” usage and practice tracks.

Image
  1. One-Chord Vamp (single harmony, funk / modal feel) "SERIOUSLY Funky Backing Track For Guitar (Dm7 Vamp)" A Dm7-only groove that keeps a steady funk feel. The track invites you to solo using D Dorian / D minor pentatonic over one repeating chord. This is a textbook example of “stay on one chord and improvise indefinitely.” "Dm7 (140 bpm) Jazz Backing Track / Single Chord Vamp" A swing-oriented Dm7 one-chord vamp. The purpose is to keep talking (soloing) over the same harmony without any form changes, which is ideal for modal phrasing. 2. Two-Chord Vamp (call-and-response tension) "TWO CHORD VAMP D DORIAN - Dm7 to G7" A two-bar loop that just cycles Dm7 → G7. The soloist shifts color between the two bars, often using D Dorian or D minor pentatonic. This is like a ii–V that never resolves, which keeps tension alive. "Funk Groove Jam Track (Dm7 to G7 Vamp 80 BPM)" A funk / fusion groove based only on Dm7 an...

"Suma no ura" (the shore of Suma, near present-day Kobe) appears in both The Tale of Genji and The Tale of the Heike, but it carries very different meanings.

Image
 "Suma no ura" (the shore of Suma, near present-day Kobe) appears in both The Tale of Genji and The Tale of the Heike, but it carries very different meanings. In The Tale of Genji, set in the mid-Heian period (around the 10th century), Prince Genji is forced to leave the capital for political reasons and temporarily lives in exile at Suma. The roaring sea, storms at night, and the lonely coast express his fall from glory, his anxiety and longing for the capital, and the turning point that will eventually lead to his return. This establishes Suma as a place of "isolation and rebirth." In The Tale of the Heike, Suma appears in the context of the Battle of Ichi-no-Tani in 1184 (Juei 3 / Jūei 3, commonly known as the Genpei War). Here, the Heike are defeated by Minamoto no Yoshitsune, retreat westward, and young warriors are killed on the beach. Suma becomes the stage of collapse and the symbol of impermanence and the fall of the proud. Both in the internal chronolog...

Reinforcement learning and evolutionary computation share the same overall goal

Image
Reinforcement learning and evolutionary computation share the same overall goal. Both try something in an environment, evaluate the result, and use that feedback to move toward better behavior or a better solution. In this sense, they are not from different worlds; they can be seen as two approaches within the same broad family of learning methods. However, the way they improve is quite different. Evolutionary computation prepares many candidate solutions (individuals) at once, tests each of them, scores them, and keeps only the high-scoring ones to form the next generation. A key point is that it does not need to analyze in detail why a certain individual was good or which specific decisions made it succeed. It simply treats the strong individual as a parent, applies crossover and mutation, and passes on that design to the next generation. In this sense, it is a method for deciding “which individual is good.” Reinforcement learning, on the other hand, focuses on training a single agen...

“Manzairaku” is one of the most prestigious celebratory pieces in gagaku (Japanese court music and dance).

Image
 “Manzairaku” is one of the most prestigious celebratory pieces in gagaku (Japanese court music and dance). It is performed as bugaku (dance with music) to praise peace in the realm and long life, and is considered an auspicious rite. Alongside “Taiheiraku,” it is presented at the most formal state ceremonies such as the enthronement of the emperor and imperial banquets. It belongs to the “left dance” tradition, meaning it traces its lineage to music and dance forms transmitted from the continent (Sui/Tang China). Rather than being a forceful military-style dance, it is a refined civil dance (hiramai), characterized by calm, dignified movement. Typically four dancers (sometimes six) enter, arrange their formation, and slowly circle the stage while spreading their sleeves in a gesture that evokes a phoenix descending as a good omen. The phoenix is said to appear in the age of a virtuous ruler and cry “ten thousand years,” blessing the ruler’s reign; this encodes a political message ...

SLAPP lawsuits are lawsuits filed by powerful actors against critics, activists, or journalists mainly to silence them.

Image
 SLAPP lawsuits are lawsuits filed by powerful actors against critics, activists, or journalists mainly to silence them. The goal is not necessarily to win on the legal merits, but to burden the target with cost, time, and fear so that they stop speaking out. This chilling effect on free expression and public oversight is considered a serious social problem.
Image
 Modern AI models (such as ChatGPT) are built on an architecture called the Transformer. It did not appear overnight; it emerged through three stages. First, LSTMs addressed RNNs’ weakness with long-range context by using gating mechanisms, making practical language understanding possible. Next, Seq2Seq with attention let models learn which parts of the input to focus on for each output step, greatly boosting translation quality. Finally, the 2017 Transformer removed recurrence and used self-attention to process all tokens in parallel, enabling both massive scale and high performance. This became the foundation of today’s large language models.