311 transistor acapella
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This song definitely gives a far away psychedelic feeling. Inner Light Spectrum- 5/5 An amazing song and one of the first that SA sings and he does and amazing job. One of the best songs on the album and probably the best 311 single to date.ĥ. Beautiful Disaster- 5/5 Great song with a hard rock intro sliding into the reggae hinting guitar solo and sliding back into the rock riff with distant almost distorted voice giving the song a far away feel. Galaxy- 4/5 One of the hardest rocking songs on the album yet as the hard intro comes in immediately SA comes in rapping giving the song another meaning and feel. The reggae mixed with the rock blends perfectly and gives a psychedelic feel ominous of 311.ģ. Many different styles blended into one known as 311. Prisoner- 5/5 Another song away form the norm. The title song gives you a taste of what the album is going to be starting with the rock intro and falling into the reggae ending. Almost every song is great and it is completely off the norm yet it is amazing.ġ. Filled with many effects and tons of totally different music the album journeys through many forms of music also combing ones not usually seen together. He concludes by discussing how and why popular music cultures have taken on many of the roles of traditional religions in contemporary society.Many critics say Transistor is one of 311 worst albums however this is 311 best album ever including the new album Don't Tread On Me. Rupert Till explores the cults of heavy metal, pop stars, club culture and virtual popular music worlds, investigating the sex, drug, local and death cults of the sacred popular, and their relationships with traditional religions.
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It provides an introduction to the history of the interactions of vernacular music and religion, and the role of music in religious culture. "Pop Cults" investigates the ways in which popular music and its surrounding culture have become a primary site for the location of meaning, belief and identity. At a time when fundamentalism is on the rise, traditional religions are in decline and postmodernity has challenged any system that claims to be all-defining, young people have left their traditional places of worship and set up their own, in clubs, at festivals and within music culture. This book explores the development of a range of cults of popular music as a response to changes in attitudes to meaning, spirituality and religion in society. These data are exploited using curriculum learning, where we see an improvement from when testing on a set of 715 songs and evaluated on a complex chord alphabet. Here we align over 1, 000 chord sequences to audio and use them as an additional training source. Another use for these sequences is in a training scenario. We find that this approach increases recognition accuracy from on a set of songs by the rock group The Beatles. In test, we align these sequences to the audio, accounting for changes in key, different interpretations, and missing structural information. This is investigated through the use of guitar chord sequences obtained from the web. Our system is also able to learn from partially-labelled data. When sufficient training examples are available, we find that our model achieves similar performance on both the well-known and novel datasets and statistically significantly outperforms a baseline Hidden Markov Model. In the months prior to the completion of this thesis, a large number of new, fully-labelled datasets have been released to the research community, meaning that the generalisation potential of models may be tested. This performance is realised by the introduction of a novel Dynamic Bayesian Net- work and chromagram feature vector, which concurrently recognises chords, keys and bass note sequences on a set of songs by The Beatles, Queen and Zweieck. In this thesis we introduce a machine learning based automatic chord recognition algorithm that achieves state of the art performance.