Smile (L'Arc-en-Ciel album)

Smile is the ninth album by L'Arc-en-Ciel, released on March 31, 2004. It was the band's first original studio album after a prolonged hiatus. Smile was later given a United States release by Tofu Records, for which all Japanese song titles received English translations. The song "Ready Steady Go" was used as the second opening for the Fullmetal Alchemist anime.

Smile
Studio album by
ReleasedMarch 31, 2004 (JP)
June 26, 2004 (US)
Genre
Length44:42
LabelKi/oon (JP)
Tofu (US)
ProducerL'Arc-en-Ciel, Hajime Okano
L'Arc-en-Ciel chronology
The Best of L'Arc-en-Ciel C/W
(2003)
Smile
(2004)
Awake
(2005)
Singles from Smile
  1. "Spirit dreams inside"
    Released: September 05, 2001
  2. ""READY STEADY GO""
    Released: February 04, 2004
  3. "Hitomi no Juunin"
    Released: March 03, 2004

Track listing

All lyrics are written by hyde, except track 3 by ken; track 5 by tetsu.

No.TitleMusicLength
1."Kuchizuke" (接吻 Kiss)ken4:25
2."Ready Steady Go"tetsu3:45
3."Lover Boy"ken4:45
4."Feeling Fine"ken4:17
5."Time Goes On"tetsu4:44
6."Coming Closer"ken5:14
7."Eien" (永遠 Forever)hyde4:39
8."Revelation"yukihiro3:18
9."Hitomi no Jūnin" (瞳の住人 Living in Your Eyes)tetsu5:55
10."Spirit Dreams Inside"hyde3:47
11."Ready Steady Go (hydeless Version)" (US release only)tetsu3:47

Personnel

  • hydevocals, acoustic guitar on track 10
  • kenguitar, backing vocals, keyboards on tracks 1, 2, 4, 5, 6, 7, 9 and 10, lap steel guitar on track 10
  • tetsubass guitar, backing vocals, keyboards on tracks 2, 5 and 9, guitar solo on track 5
  • yukihirodrums, percussion, backing vocals, metal percussion on track 3
  • Hajime Okano – keyboards on tracks 2, 4, 5, 9 and 10, backing vocals on track 8
  • Hiroaki Sugawara – keyboards on track 6, synthesizer on track 10
  • Asuka Kaneko – strings on track 6
  • Jack Danger – backing vocals on track 8
  • Hal-Oh Togashi – acoustic piano
gollark: The image is just 3 matrices of R/G/B values.
gollark: There are 129057189471894718247141491807401825701892912 random details and things but that's the gist of it.
gollark: Then, you just move it a little bit toward lower loss (gradient descent).
gollark: You have a big thing of settable parameters determining how you go from input to output. And if you know what the result *should* be (on training data), then as the maths is all "differentiable", you can differentiate it and get the gradient of loss wrt. all the parameters.
gollark: Well, you put your data into something something linear algebra and something something gradient descent, and answers come out.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.