v-ing全解(廖)_图文_百度文库
两大类热门资源免费畅读
续费一年阅读会员，立省24元！
评价文档：
v-ing全解(廖)
上传于||暂无简介
大小：899.00KB
登录百度文库，专享文档复制特权，财富值每天免费拿！
你可能喜欢Pages: 1/2
主题 : Bitcode是什么，如何配置？
级别: 新手上路
可可豆: 20 CB
威望: 20 点
在线时间: 0(时)
发自: Web Page
来源于&&分类
Bitcode是什么，如何配置？&&&
       今天在一个麦子学院上看到一篇关于第三方库不包含bitcode就会报错的文章，感觉剖析得很详细，分享出来，希望可以对iOS初入门者有所帮助。下面我们就一起来看看吧。       用Xcode 7 beta 3在真机(iOS 8.3)上运行一下工程，结果发现工程编译不过。看了下问题，报的是以下错误：ld: ‘/Users/**/Framework/SDKs/PolymerPay/Library/mobStat/lib**SDK.a(**ForSDK.o)’ does not contain bitcode. You must rebuild it with bitcode enabled (Xcode setting ENABLE_BITCODE), obtain an updated library from the vendor, or disable bitcode for this target. for architecture arm64     得到的信息是引入的一个第三方库不包含bitcode。Bitcode是什么？       查阅了一下官方文档，在 App Distribution Guide – App Thinning (iOS, watchOS) 一节中，找到了下面这样一个定义：Bitcode is an intermediate representation of a compiled program. Apps you upload to iTunes Connect that contain bitcode will be compiled and linked on the App Store. Including bitcode will allow Apple to re-optimize your app binary in the future without the need to submit a new version of your app to the store.         说的是bitcode是被编译程序的一种中间形式的代码。包含bitcode配置的程序将会在App store上被编译和链接。bitcode允许苹果在后期重新优化程序的二进制文件，而不需要重新提交一个新的版本到App store上。        而在 What’s New in Xcode-New Features in Xcode 7 中，还有一段如下的描述：Bitcode. When you archive for submission to the App Store, Xcode will compile your app into an intermediate representation. The App Store will then compile the bitcode down into the 64 or 32 bit executables as necessary.        当提交程序到App store上时，Xcode会将程序编译为一个中间表现形式(bitcode)。然后App store会再将这个botcode编译为可执行的64位或32位程序。        再看看这两段描述，都是放在App Thinning(App瘦身)一节中，可以看出其与包的优化有关了。Bitcode配置          在上面的错误提示中，提到了如何处理我们遇到的问题：You must rebuild it with bitcode enabled (Xcode setting ENABLE_BITCODE ), obtain an updated library from the vendor, or disable bitcode for this target. for architecture arm64要么让第三方库支持，要么关闭target的bitcode选项。&&&&&&&& 实际上，在Xcode 7中，我们新建一个iOS程序时，bitcode选项默认是设置为YES的。我们可以在”Build Settings”-&”Enable Bitcode”选项中看到这个设置。不过，我们现在需要考虑的是三个平台：iOS，Mac OS，watchOS。对于iOS，bitcode是可选的；对于watchOS，bitcode是必须的；而Mac OS是不支持bitcode。&&&&&&&&所以，如果我们的工程需要支持bitcode，则必要要求所有引入的第三方库都支持bitcode。&& &&&&&&&&通过本文对bitcode的概念及配置情况的简要介绍，希望iOS开发人员在工程运行中遇到类似的情况，可以根据上文的介绍更有效的找到原因并及时处理。
级别: 新手上路
可可豆: 2 CB
威望: 2 点
在线时间: 27(时)
发自: Web Page
还蛮具体 不得少于10各自
级别: 新手上路
可可豆: 5 CB
威望: 5 点
在线时间: 7(时)
发自: Web Page
谢谢啦！这个玩意应该是起到这个作用的吧？我们把我们的App以 bitCode 形式上传到AppStore后，苹果再处理一次，这样不同架构的手机只下载他对应的那部分资源就好（32位的没必要下载64位的那些内容，反之同理）起到给安装包瘦身的目的。
级别: 侠客
可可豆: 434 CB
威望: 434 点
在线时间: 106(时)
发自: Web Page
把bitcode设为no不会影响上线吧
级别: 侠客
UID: 347776
可可豆: 267 CB
威望: 215 点
在线时间: 1039(时)
发自: Web Page
回 3楼(李美琪) 的帖子
不会影响上线的。 主要是为了苹果能 通过 app thining&&瘦身 app
_____________我就静静的看着你装逼&&从来不会打断你
级别: 侠客
可可豆: 434 CB
威望: 434 点
在线时间: 106(时)
发自: Web Page
回 4楼(cocosol) 的帖子
噢 我知道了 谢了
级别: 新手上路
UID: 498729
可可豆: 12 CB
威望: 6 点
在线时间: 138(时)
发自: Web Page
级别: 侠客
UID: 363290
可可豆: 333 CB
威望: 280 点
在线时间: 575(时)
发自: Web Page
原来是这样
我是一只菜菜菜菜鸟，怎么飞也想飞得更高!
级别: 新手上路
UID: 522042
可可豆: 28 CB
威望: 12 点
在线时间: 36(时)
发自: Web Page
豁然开朗，完善的解决了我的问题……
级别: 侠客
UID: 471640
可可豆: 439 CB
威望: 304 点
在线时间: 400(时)
发自: Web Page
像调用了友盟sdk，微信和支付宝第三方支付这些都需要把这个值设置为NO,他们都不支持。
Pages: 1/2
关注本帖(如果有新回复会站内信通知您)
9*6-8 正确答案:46
发帖、回帖都会得到可观的积分奖励。
按"Ctrl+Enter"直接提交
关注CocoaChina
关注微信 每日推荐
扫一扫 浏览移动版动词的ing 形式做主语和宾语_百度文库
两大类热门资源免费畅读
续费一年阅读会员，立省24元！
动词的ing 形式做主语和宾语
上传于||暂无简介
阅读已结束，如果下载本文需要使用0下载券
想免费下载更多文档？
定制HR最喜欢的简历
下载文档到电脑，查找使用更方便
还剩2页未读，继续阅读
定制HR最喜欢的简历
你可能喜欢From Wikipedia, the free encyclopedia
For other uses of "8-bit music", see .
An analogue signal (in red) encoded to 4-bit PCM digital samples (in blue); the bit depth is four, so each sample's amplitude is one of 16 possible values.
(PCM), bit depth is the number of
of information in each , and it directly corresponds to the resolution of each sample. Examples of bit depth include , which uses 16 bits per sample, and
which can support up to 24 bits per sample.
In basic implementations, variations in bit depth primarily affect the noise level from —thus the
(SNR) and . However, techniques such as ,
mitigate these effects without changing the bit depth. Bit depth also affects
and file size.
Bit depth is only meaningful in reference to a PCM . Non-PCM formats, such as
formats, do not have associated bit depths. For example, in , quantization is performed on PCM samples that have been transformed into the .
A PCM signal is a sequence of digital audio samples containing the data providing the necessary information to
the original . Each sample represents the
of the signal at a specific point in time, and the samples are uniformly spaced in time. The amplitude is the only information explicitly stored in the sample, and it is typically stored as either an
number, encoded as a
with a fixed number of digits: the sample's bit depth.
The resolution of binary integers increases
as the word length increases. Adding one bit doubles the resolution, adding two quadruples it and so on. The number of possible values that can be represented by an integer bit depth can be calculated by using , where n is the bit depth. Thus, a
system has a resolution of 65,536 (216) possible values.
PCM audio data is typically stored as
numbers in
Many audio
(DAWs) now support PCM formats with samples represented by
numbers. Both the
file format and the
file format support floating point representations.
Unlike integers, whose bit pattern is a single series of bits, a floating point number is instead composed of separate fields whose mathematical relation forms a number. The most common standard is
which is composed of three bit patterns: a
which represents whether the number is positive or negative, an exponent and a
which is raised by the exponent. The mantissa is expressed as a
in IEEE base-two floating point formats.
The bit depth limits the
(SNR) of the reconstructed signal to a maximum level determined by
error. The bit depth has no impact on the , which is constrained by the .
Quantization noise is a
of quantization error introduced by the
process during
(ADC). It is a rounding error between the analog input voltage to the ADC and the output digitized value. The noise is
and signal-dependent.
binary number (149 in ), with the LSB highlighted
In an ideal ADC, where the quantization error is uniformly distributed between
{\displaystyle \scriptstyle {\pm {\frac {1}{2}}}}
(LSB) and where the signal has a uniform distribution covering all quantization levels, the
(SQNR) can be calculated from
{\displaystyle \mathrm {SQNR} =20\log _{10}(2^{Q})\approx 6.02\cdot Q\ \mathrm {dB} \,\!}
where Q is the number of quantization bits and the result is measured in
digital audio has a theoretical maximum SNR of 144 dB, compared to 96 dB for 16- however, as of 2007 digital audio converter technology is limited to a SNR of about 124 dB (21-bit) because of real-world limitations in
design. Still, this approximately matches the performance of the human .
Signal-to-noise ratio and resolution of bit depths
Possible integer values (per sample)
Base-ten signed range (per sample)
24.08 dB
48.16 dB
-128 to +127
66.22 dB
-1024 to +1023
96.33 dB
-32,768 to +32,767
120.41 dB
-524,288 to +524,287
144.49 dB
16,777,216
-8,388,608 to +8,388,607
192.66 dB
4,294,967,296
-2,147,483,648 to +2,147,483,647
288.99 dB
281,474,976,710,656
-140,737,488,355,328 to +140,737,488,355,327
385.32 dB
18,446,744,073,709,551,616
-9,223,372,036,854,775,808 to +9,223,372,036,854,775,807
The resolution of floating point samples is less straightforward than integer samples, but the benefit comes in the increased accuracy of low values. In floating point representation, the space between any two adjacent values is of the same proportion as the space between any other two adjacent values, whereas in an integer representation, the space between adjacent values gets larger in proportion to low-level signals. This greatly increases the SNR because the accuracy of a high-level signal will be the same as the accuracy of an identical signal at a lower level.
The trade-off between floating point and integers is that the space between large floating point values is greater than the space between large integer values of the same bit depth. Rounding a large floating point number results in a greater error than rounding a small floating point number whereas rounding an integer number will always result in the same level of error. In other words, integers have round-off that is uniform, always rounding the LSB to 0 or 1, and floating point has SNR that is uniform, the quantization noise level is always of a certain proportion to the signal level. A floating point noise floor will rise as the signal rises and fall as the signal falls, resulting in audible variance if the bit depth is low enough.
Most processing operations on digital audio involve requantization of samples, and thus introduce additional rounding error analogous to the original quantization error introduced during analog to digital conversion. To prevent rounding error larger than the implicit error during ADC, calculations during processing must be performed at higher precisions than the input samples.
(DSP) operations can be performed in either
or floating point precision. In either case, the precision of each operation is determined by the precision of the hardware operations used to perform each step of the processing and not the resolution of the input data. For example, on
processors, floating point operations are performed at 32- or 64-bit precision and fixed point operations at 16-, 32- or 64-bit resolution. Consequently, all processing performed on Intel-based hardware will be performed at 16-, 32- or 64-bit integer precision, or 32- or 64-bit floating point precision regardless of the source format. However, if memory is at a premium, software may still choose to output lower resolution 16- or 24-bit audio after higher precision processing.
Fixed point
often support unusual word sizes and precisions in order to support specific signal resolutions. For example, the
DSP chip uses 24-bit word sizes, 24-bit multipliers and 56-bit accumulators to perform
on two 24-bit samples without overflow or rounding. On devices that do not support large accumulators, fixed point operations may be implicitly rounded, reducing precision to below that of the input samples.
Errors compound through multiple stages of DSP at a rate that depends on the operations being performed. For uncorrelated processing steps on audio data without a DC offset, errors are assumed to be random with zero mean. Under this assumption, the standard deviation of the distribution represents the error signal, and quantization error scales with the square root of the number of operations. High levels of precision are necessary for algorithms that involve repeated processing, such as . High levels of precision are also necessary in recursive algorithms, such as
(IIR) filters. In the particular case of IIR filters, rounding error can degrade frequency response and cause instability.
Main article:
The noise introduced by quantization error, including rounding errors and loss of precision introduced during audio processing, can be mitigated by adding a small amount of random noise, called , to the signal before quantizing. Dithering eliminates the granularity of quantization error, giving very low distortion, but at the expense of a slightly raised . Measured using , this is about 66 dB below , or 84 dB below digital , which is somewhat lower than the microphone noise level on most recordings, and hence of no consequence in 16-bit audio (see
for more on this).
24-bit audio does not require dithering, as the noise level of the digital converter is always louder than the required level of any dither that might be applied. 24-bit audio could theoretically encode 144 dB of dynamic range, but based on manufacturer's datasheets no ADCs exist that can provide higher than ~125 dB.
Dither can also be used to increase the effective dynamic range. The perceived dynamic range of 16-bit audio can be 120 dB or more with
, taking advantage of the frequency response of the human ear.
is the difference between the largest and smallest signal a system can record or reproduce. Without dither, the dynamic range correlates to the quantization noise floor. For example, 16-bit integer resolution allows for a dynamic range of about 96 dB.
Using higher bit depths during
accommodates greater dynamic range. If the signal's dynamic range is lower than that allowed by the bit depth, the recording has , and the higher the bit depth, the more headroom that's available. This reduces the risk of
without encountering quantization errors at low volumes.
With the proper application of dither, digital systems can reproduce signals with levels lower than their resolution would normally allow, extending the effective dynamic range beyond the limit imposed by the resolution.
The use of techniques such as
can further extend the dynamic range of sampled audio by moving quantization error out of the frequency band of interest.
Main article:
Oversampling is an alternative method to increase the dynamic range of PCM audio without changing the number of bits per sample. In oversampling, audio samples are acquired at a multiple of the desired sample rate. Because quantization error is assumed to be uniformly distributed with frequency, much of the quantization error is shifted to ultrasonic frequencies, and can be removed by the
during playback.
For an increase equivalent to n additional bits of resolution, a signal must be oversampled by
{\displaystyle \mathrm {number\ of\ samples} =(2^{n})^{2}=2^{2n}.}
For example, a 14-bit ADC can produce 16-bit 48 kHz audio if operated at 16× oversampling, or 768 kHz. Oversampled PCM therefore exchanges fewer bits per sample for more samples in order to obtain the same resolution.
Dynamic range can also be enhanced with oversampling at signal reconstruction, absent oversampling at the source. Consider 16× oversampling at reconstruction. Each sample at reconstruction would be unique in that for each of the original sample points sixteen are inserted, all having been calculated by the digital signal processor (FIR digital filter) as time interpolation. This is not linear interpolation. The mechanism of lowered noise floor is as previously discussed, that is, quantization noise power has not been reduced, but the noise spectrum has been spread over 16× the audio bandwidth.
Historical note—The compact disc standard was developed by a collaboration between Sony and Phillips. The first Sony consumer unit featured a 16-bit DAC; the first Phillips units dual 14-bit DACs. This caused confusion in the marketplace and even in professional circles. Years after, one of the electronic engineering trade journals mistakenly made a historical note of the 14-bit DACs in the Phillips unit as allowing 84 dB SNR, as the writer was either unaware that the specifications of the unit indicated 4× oversampling or unaware of the implication. It was correctly noted that Phillips had no OEM sourced 16-bit DAC at the time, but the writer was not cognizant of the power of digital signal processing to increase the audio SNR to 90 dB.
Main article:
Oversampling a signal results in equal quantization noise per unit of bandwidth at all frequencies and a dynamic range that improves with only the square root of the oversampling ratio. Noise shaping is a technique that adds additional noise at higher frequencies which cancels out some error at lower frequencies, resulting in a larger increase in dynamic range when oversampling. For nth-order noise shaping, the dynamic range of an oversampled signal is improved by an additional 6n dB relative to oversampling without noise shaping. For example, for a 20 kHz analog audio sampled at 4× oversampling with second order noise shaping, the dynamic range is increased by 30 dB. Therefore, a 16-bit signal sampled at 176 kHz would have equal resolution as a 21-bit signal sampled at 44.1 kHz without noise shaping.
Noise shaping is commonly implemented with . Using delta-sigma modulation,
obtains 120 dB SNR at audio frequencies using 1-bit audio with 64× oversampling.
Bit depth is a fundamental property of digital audio implementations and there are a variety of situations where it is a measurement.
Example applications and bits per sample
Application
Description
Audio format(s)
(Red Book)
Digital media
Digital media
16-, 20- and 24-bit LPCM
Super Audio CD
Digital media
Digital media
16-, 20- and 24-bit LPCM and others
Digital media
12-bit compressed PCM and 16-bit uncompressed PCM
Recommendation
Compression standard for
8-bit PCM with
-1, NICAM-2 and NICAM-3
Compression standards for
10-, 11- and 10-bit PCM respectively, with companding
"All sample data is maintained internally in 32 bit floating point format..."
Pro Tools 11
16- and 24-bit or 32-bit floating point sessions and 64-bit floating point
16- and 24-bit projects
32-bit floating point mixing
16-, 20- and 24-bit I/O, 32-bit floating point arithmetic and 64-bit summing
8-bit PCM, 16-bit PCM, 24-bit PCM, 32-bit PCM, 32-bit FP, 64-bit FP, 4-bit IMA ADPCM & 2-bit cADPCM ;
8-bit int, 16-bit int, 24-bit int, 39-bit int, 32-bit float, and 64-bit float
'11 (version 6)
DAW by Apple Inc.
16-bit default with 24-bit real instrument recording
Open source audio editor
16- and 24-bit LPCM and 32-bit floating point
Bit depth affects
and file size. Bit rate refers to the amount of data, specifically bits, transmitted or received per second.
—corresponding concept for digital images
DVD-Audio also supports optional , a
Blu-ray supports a variety of non-LPCM formats but all conform to some combination of 16, 20 or 24 bits per sample.
ITU-T specifies the
companding algorithms, compressing down from 13 and 14 bits respectively.
NICAM systems 1, 2 and 3 compress down from 13, 14 and 14 bits respectively.
Thompson, Dan (2005). Understanding Audio. Berklee Press. .
Smith, Julius (2007). . Mathematics of the Discrete Fourier Transform (DFT) with Audio Applications, Second Edition, online book 2012.
Campbell, Robert (2013). . Cengage Learning 2013.
Wherry, Mark (March 2012). . Sound On Sound 2013.
Price, Simon (October 2005). . Sound On Sound 2013.
. Ableton. .
Kabal, Peter (3 January 2011). . McGill University 2013.
Kabal, Peter (3 January 2011). . McGill University 2013.
Smith, Steven (1997–98). .
Kester, Walt (2007).
D. R. Campbell.
(PDF) 2011. The dynamic range of human hearing is [approximately] 120 dB
from the original on 4 June . The practical dynamic range could be said to be from the threshold of hearing to the threshold of pain [130 dB]
Smith, Steven (1997–98). .
Moorer, James (September 1999).
Tomarakos, John. . . Analog Systems 2013.
(PDF). Freescale 2013.
Smith, Steven (1997–98).
Carletta, Joan (2003). "Determining Appropriate Precisions for Signals in Fixed-Point IIR Filters". DAC. : .
– "I went looking for the best dynamic range audio ADC I could find" and highest are 123 dB dynamic range
(25 March 2012). . xiph.org 2013. With use of shaped dither, which moves quantization noise energy into frequencies where it's harder to hear, the effective dynamic range of 16 bit audio reaches 120dB in practice, more than fifteen times deeper than the 96dB claim. 120dB is greater than the difference between a mosquito somewhere in the same room and a jackhammer a foot away.... or the difference between a deserted 'soundproof' room and a sound loud enough to cause hearing damage in seconds. 16 bits is enough to store all we can hear, and will be enough forever.
Stuart, J. Robert (1997).
(PDF). Meridian Audio Ltd. One of the great discoveries in PCM was that, by adding a small random noise (that we call dither) the truncation effect can disappear. Even more important was the realisation that there is a right sort of random noise to add, and that when the right dither is used, the resolution of the digital system becomes infinite.
(PDF). e2v Semiconductors. .
Kester, Walt.
(PDF). Analog Devices 2013.
. . Sweetwater. 27 April .
(PDF). Sonic Solutions. Archived from
(PDF) on 4 March .
Shapiro, L. (2 July 2001). . ExtremeTech 2013.
(PDF). Blu-ray Disc Association. April .
Puhovski, Nenad (April 2000). . www.stanford.edu. Archived from
on 27 October .
(PDF). International Telecommunications Union 2013.
(PDF). BBC Research Department. August 1978. Archived from
(PDF) on 8 November .
. Ardour Community. .
(ZIP/PDF). Avid. .
(PDF). Apple. January .
(PDF). Propellerhead Software. .
. Apple. 13 March .
. . Audacity development team 2014.
Ken C. Pohlmann (15 February 2000). Principles of Digital Audio (4th ed.). McGraw-Hill Professional.  .
: Hidden categories: