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Titlebook: I Got Something to Say; Gender, Race, and So Matthew Oware Book 2018 The Editor(s) (if applicable) and The Author(s) 2018 Hip-Hop.Race evas

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发表于 2025-3-21 18:02:22 | 显示全部楼层 |阅读模式
书目名称I Got Something to Say
副标题Gender, Race, and So
编辑Matthew Oware
视频video
概述Includes a lyrical (content) analysis of recent rap music of men and women from 2005 to 2015.Analyzes commercial and underground/independent rap music in the United States.Discusses the emergence of q
图书封面Titlebook: I Got Something to Say; Gender, Race, and So Matthew Oware Book 2018 The Editor(s) (if applicable) and The Author(s) 2018 Hip-Hop.Race evas
描述What do millennial rappers in the United States say in their music? This timely and compelling book answers this question by decoding the lyrics of over 700 songs from contemporary rap artists. Using innovative research techniques, Matthew Oware reveals how emcees perpetuate and challenge gendered and racialized constructions of masculinity, femininity, and sexuality. Male and female artists litter their rhymes with misogynistic and violent imagery. However, men also express a full range of emotions, from arrogance to vulnerability, conveying a more complex manhood than previously acknowledged. Women emphatically state their desires while embracing a more feminist approach. Even LGBTQ artists stake their claim and express their sexuality without fear. Finally, in the age of Black Lives Matter and the presidency of Donald J. Trump, emcees forcefully politicize their music. Although complicated and contradictory in many ways, rap remains a powerful medium for social commentary..
出版日期Book 2018
关键词Hip-Hop; Race evasion; post-feminism; postmodernism; underground rap; conscious rap; streaming music; Women
版次1
doihttps://doi.org/10.1007/978-3-319-90454-2
isbn_softcover978-3-030-08019-8
isbn_ebook978-3-319-90454-2
copyrightThe Editor(s) (if applicable) and The Author(s) 2018
The information of publication is updating

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发表于 2025-3-21 22:06:38 | 显示全部楼层
Matthew Oware(T-F) units with local signal-to-noise ratio (SNR) exceeding a local criterion (LC) are assigned the binary value 1 in the binary mask. However, there are two problems with employing IBM in source separation applications. First, an optimum LC for a certain SNR may not be appropriate for other SNRs.
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发表于 2025-3-22 06:37:48 | 显示全部楼层
Matthew Oware(T-F) units with local signal-to-noise ratio (SNR) exceeding a local criterion (LC) are assigned the binary value 1 in the binary mask. However, there are two problems with employing IBM in source separation applications. First, an optimum LC for a certain SNR may not be appropriate for other SNRs.
发表于 2025-3-22 11:40:59 | 显示全部楼层
Matthew Owarerent representation domains, such as time domain, frequency domain or time-frequency domain. In recent years, sparsity has been widely exploited to solve the problem of underdetermined blind source separation (UBSS), where the number of sources exceeds that of the observed mixtures. In fact, the spa
发表于 2025-3-22 16:36:27 | 显示全部楼层
发表于 2025-3-22 17:23:49 | 显示全部楼层
Matthew Owarend we discuss its applicability to acoustic signal processing. The term “SIMO” represents a specific transmission system in which the input is a single source signal and the outputs are its transmitted signals observed at multiple microphones. The SIMO-ICA consists of multiple ICAs and a fidelity co
发表于 2025-3-23 00:50:09 | 显示全部楼层
Matthew Owarer of nonzero elements is smaller than might be expected. This is a surprisingly powerful idea, as the ability to express a signal sparsely in some known, and potentially overcomplete, basis constitutes a strong model, while also lending itself to efficient algorithms. In the framework we explore, th
发表于 2025-3-23 02:17:39 | 显示全部楼层
Matthew Owaremakes it important to be able to separate and extract a target speech signal from noisy observations for both man–machine and human–human communication. Blindsourceseparation(BSS)isanapproachforestimatingsourcesignals using only information about their mixtures observed in each input channel. The es
发表于 2025-3-23 09:30:09 | 显示全部楼层
e source signals should be extracted from a single stream of observations. To overcome the mathematical intractability, prior information on the source characteristics is generally assumed and applied to derive a source separation algorithm. This chapter describes one of the monaural source separati
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