http://www.informationweek.com/story/showArticle.jhtml?articleID=191601137&cid=RSSfeed_IWK_All

Study: Tech Replaces Diamonds As Girl's Best Friend

Women, on average, own nearly as much technology as their male counterparts and would appreciate a digital video camera more than a pair of designer shoes.

By Reuters
InformationWeek

Aug 1, 2006 05:14 PM

NEW YORK - Diamonds are no longer a girl's best friend, according to a new U.S. study that found three of four women would prefer a new plasma TV to a diamond necklace.

The survey, commissioned by U.S. cable television's Oxygen Network that is owned and operated by women, found the technology gender gap has virtually closed with the majority of women snapping up new technology and using it easily.

Women were found on average to own 6.6 technology devices while men own 6.9, and four out of every five women felt comfortable using technology with 46 percent doing their own computer trouble-shooting.

"People make the assumption that women are not as advanced as men when it comes to technology and I was surprised at the parity men and women now have in terms of technology," Geraldine Laybourne, chairman and chief executive of Oxygen Network, told Reuters.

The Girls Gone Wired survey of 1,400 women and 700 men aged 15 to 49, which was conducted by market researcher TRU, found that given the choice, women would opt for tech items rather than luxury items like jewelry or vacations.

The study found 77 percent of women surveyed would prefer a new plasma television to a diamond solitaire necklace and 56 percent would opt for a new plasma TV over a weekend vacation in Florida.

Even shoes lost out. The study found 86 percent would prefer a new digital video camera to a pair of designer shoes.

The study found over the next five years women see themselves increasing their activities in six tech areas: digital cameras, cell phones, e-mail, camera phones, text messaging and instant messaging.

Laybourne said this increasing use of technology among women was expected to continue -- with advertisers needing to ensure they addressed women's increased usage and knowledge.

"Women don't feel like they have been given credit for what they know and they are condescended to," Laybourne said.

Copyright 2006 Reuters. Click for Restrictions

 

Present and Future

The Present

It sounds like everything is apples so far. User-centered design works well, we have good office information systems, HCI is a solid discipline (if unexciting because we still like those breakthroughs every few years). So why write an article on the future of HCI, and more to the point, why should you read it? The beef is that IT is not just about office work any more. It's going everywhere (yes, you've heard that, but this time it really is). Because of that, we're due for another revolution (in fact, probably several) in HCI over the next few years.

Let's start with PCs. Where are they now? Intel recently reorganized itself to align with the major market sectors for Intel PCs today. Those sectors are office, home, medical, and mobile. That's a lot of PCs in new places, and they're almost all running a Star-style WIMP interface.

What about cellphones? Global cellphone sales are now running at 800 million units per year, about four times the annual sales of PCs (or television sets). Recent years have seen 100 percent annual growth in overall phone sales, and close to 200 percent for smart phones. Sales are nearing saturation in developed countries, but still accelerating in the Third World, which dominates now. Smart-phone sales are about 15 percent of the market now (around 100 million units), but with their faster growth should outnumber PCs by 2008. Smart phones today are about as powerful as a midrange PC from eight years ago, but they waste the latter in media performance. Although only a tiny amount of smart-phone software is around now, it is one of the fastest-growing sectors of the industry. Unfortunately, if you've tried interacting with a nontrivial smart-phone application, you'll know what an ordeal it can be. There has been a brave effort to evolve it from its WIMP interface roots, but it just feels wrong - like a shark in a shopping mall.

A small army of gadgets are fighting for dominance in your living room. If you have a state-of-the-art cable box (which will also record 40 hours of hi-def TV), you know it has all the hardware (but not the software - yet) to connect to any conceivable media device. It has an always-on Internet connection and automatic software upgrades that give it a powerful marketing edge. You'll always get cool new services whether you ask for them or not. Microsoft and Apple have PC-like entries for this market, some high-end TVs include all this in the box, and then of course there are game boxes that pack most of those functions along with super-high-end graphics. I've made myself a guinea pig for this stuff, but it's really a pain to use. The wireless keyboards, cornucopia of remote controls, on-screen letter-of-the-alphabet menus - it's like those early "horseless carriage" steam automobiles that had reins. Once again, something feels really wrong.

The story is similar for the other new markets for IT: medical, automotive, etc. In all cases, we're adapting designs that were beautifully optimized for the office to a completely different environment. If the past is any lesson, that isn't going to work.

The Future: Context-Awareness

What will work in these new domains? The race is certainly not over, but there are some very good bets. Let's start with the cellphone. It has a tiny screen with tiny awkward buttons and no mouse. From start to finish, it was designed for speech. The microphone and speaker are small but highly evolved, and the mic placement in its normal position is optimal for speech recognition. We'll get to speech interfaces shortly. If it's a smart phone, it probably also has a camera and a Bluetooth radio. It has some kind of position information, ranging from coarse cell tower to highly accurate assisted satellite GPS.

This is all "context" information, in contrast to the "text" you might type on the keyboard or see on the screen. Normally, WIMP interfaces rely entirely on the text you type (let's include mouse input) to figure out what to do. Context-aware interfaces use everything they can. This is particularly relevant to mobile phones. When you're using a phone, you're either in some "place" (café, restaurant, store) where you do rather specific activities, or you're moving between places. If the phone can figure out what that place is, it can also provide services that you want there, or that complement services that that place provides (e.g., song previews in a music store, comparison pricing in a supermarket, stats or replays at a baseball game). When you're between places, the phone can use other pieces of context to figure out what services to offer, or it can wait for you to ask.

Let's work through a concrete example: It's 7 p.m., it's raining, and you're walking in San Francisco (you're from out of town). You open your phone and it displays three buttons labeled "Dinner?", "Taxi?", and "Rapid transit?". Selecting "Dinner?" will present restaurants you're apt to like (using collaborative filtering) and even dishes that you may want. The other options leverage the fact that the phone "knows" that you aren't driving and that it's raining. It also selects "Rapid transit?" (using that name rather than BART as locals know it, since you're not local), rather than bus or tram options since it knows your destination and/or because BART is easier to figure out for out-of-towners than the MUNI bus and tram system. The system's "smarts" are built on knowledge of other users' behavior, knowledge of your own behavior history and preferences, and the immediate context, which includes time, place, weather, Bluetooth neighborhood, etc. These three pieces represent the three fundamental facets of context that we use in all our work: immediate context; activity context, which is about the history of the particular user and a few others (because many activities are cooperative); and situational context, which is about how other actors typically behave in that situation.

Context-awareness is a dream for marketers. Imagine this: Instead of the user initiating the request for "Dinner?", the phone beeps and presents a message, "Aqua restaurant (a leading San Francisco seafood restaurant) is two blocks away and has a special on salmon-in-parchment for $20." Now, I'm a very rational person, but I also have a weakness for the pink fish, and when I'm tired and wet and I see that, it really doesn't matter what the other options are. That is an example of a proactive service, which if executed right, should be a boon to both consumers and advertisers. Before you raise the specter of a Minority Report-style advertising assault, I should tell you that I don't expect to let just anyone send that kind of message to my phone. I'm going to charge a lot for that (probably in whole dollars), so an advertiser had better be very sure of a conversion before trying it. If so, then I am likely to use that service at that time, and then it's very useful to me. If Aqua restaurant beacons this message to a few seafood-loving out-of-towners in the neighborhood that night and gets two or three conversions, then the restaurant will be ahead. If I get a half-dozen of those in an evening and one of them gives me a good service, then I feel like I've won. If none of them works out, well then at least I've earned my BART (rapid transit) fare home, and some change.

The technical challenges with making this work well are arbitrarily deep, and many of them do not fall within traditional HCI. They span a large fraction of the scope of Web 2.0 business: rich user history; highly personalized, coupled services; carefully targeted marketing; and social and individual services. It's also absolutely essential to build these systems on a deep understanding of users' behavior, their needs and wants, and the contexts where those services are used, which is where HCI methods come in. It also taps deeply into AI (for user and social modeling and prediction); systems engineering (building and deploying the services); psychology, economics, and other social sciences (for understanding rational and nonrational user behavior); and a very broad notion of security (attacks include "bleeding" advertiser revenue using robots). These challenges are going to engage developers and researchers for decades to come. Since targeted marketing is the source that feeds Web 2.0 companies, improvements here are felt directly (and quickly) on the bottom line. Since there seems to be an arbitrarily deep well for improvements, this is where Web 2.0 companies are going to be putting their attention and resources for a long time.

 

Interfaces

The Future: Perceptual Interfaces

The other important piece of future interfaces should be "perception." The simplest example is speech recognition, or more accurately, speech-based interfaces. Another example is computer vision. Smart phones are excellent speech platforms, as already noted, but most also have cameras and a respectable amount of CPU power, especially in their digital signal processors. They are more than capable of computer vision using either still images or video from their cameras. A simple example is barcode recognition, which is already available on some camera phones (both 2D and 1D barcode readers have appeared on commercial phones). OCR (optical character recognition) for business-card recognition is also available commercially. Another example is TinyMotion, a phone software application that my lab has developed, which uses the video from a camera phone to compute the phone's motion relative to a background - just as an optical mouse does. This creates a software-only general-purpose 2D mouse for camera phones. TinyMotion is very useful for map browsing (which is why we developed it) in location-based cellphone services. It turned out also to be a nice interface for smart-phone games, which is probably a bigger market than its target.

Computer vision has a big role to play in managing personal media assets, and this reaches into the home, as well as the mobile market.

These niche applications for vision on phones are suggestive, but perhaps not really convincing of the economic value of computer vision for phones. Let's look for a moment at "social media," personal data such as photos and videos that are shared with friends and family. As argued before, the phone is a communicating and social platform, and photo sharing is likely to be one of the most popular uses of multimedia on the phone. With collaborators at Berkeley and in industry, we explored face recognition from camera-phone images. The application is precisely photo-sharing and archival. The user will likely want to share a photo with the people who are in the photo and would like meta-data about who is in the photo so he or she can find it later when looking for specific people. Our results were interesting because we found not only was it possible to recognize subjects reasonably well using computer vision, but also that the recognition accuracy improved significantly when context data was used, as well as computer vision. While our system actually did its recognition on a PC rather than on the phone, we realized that the same state-of-the-art PC algorithms could easily have run on the smart phones we had used. Computer vision has a big role to play in managing personal media assets, and this reaches into the home, as well as the mobile market.

Turning to ASR (automatic speech recognition) and VUIs (voice user interfaces), we saw a boom in these industries in 2000, followed by a contraction for several years. But 2000 was also the era of wild promises and unrealistic expectations. What should have happened with speech? First of all, when PCs were mostly in offices, VUIs didn't make much sense. Nothing wrong with the technology, but speech is a poor match for most office work. Let's not forget the significant advantages of text for routine business communication: You can scan text for what you want, you can read back and forth if you don't understand, you can edit text while you're writing it to make sure you say exactly what you mean, and you can forward text through a long chain of readers without losing its meaning. Written text is generally less ambiguous than spoken language that expresses the same meaning - we're not really aware of this, but we're trained from an early age to take more care with text. Furthermore, you can work on text documents without your neighbors listening in. Much knowledge work is about managing structured or semi-structured information (even before computers came along). Most organizations relied on paper to store and move this information around with precision and robustness (again before computers). Speech technology can certainly play a role, but it's wrong to think about displacing most of the "paperwork" in office environments. As Jordan Cohen (formerly of VoiceSignal, now of SRI International) points out in his interview in this issue, the way to succeed with speech technology is first to identify the market where it makes sense.

Let's remember the lessons from the Xerox Star. The Star was all about having a real-use context (office work) and identifying an appropriate set of user tasks. Phones are primarily about communicating using a variety of media (sound, images, text) and to an increasing extent about sharing and archiving those media. To support and augment those communication services, we need some knowledge of what's "in" those media, which is exactly a machine perception task. Furthermore, if phones are to provide other services (besides communication) to users, they also need to interpret the user's intent through whatever interfaces the phone possesses. I already remarked on users' toils with phone menus and buttons, while at the same time the phone is a beautifully evolved speech platform. Speech interfaces do indeed look like a great choice. They continue to improve in performance, but the state of the art is much better than people realize.

Until last year, like most HCI researchers, I was skeptical about the value of speech interfaces in HCI. But then I saw a Samsung phone (P207) shipping with large-vocabulary speech recognition and getting very good user reviews in all kinds of publications (including the hard-to-impress business market).

I also taught a class on medical technologies and had a chance to meet with many caregivers. There is already a large speech industry in medicine, and it is widely seen as one of the key technologies moving forward (it has probably already eclipsed "office ASR" and is a significant part of the speech recognition industry overall).

I had committed the cardinal sin of generalizing experience from a technology in one context (VUIs in the office) to its application in a different context. It's the technology-in-context complex that matters. ASR-on-phones and ASR-in-medicine are brand new markets. Their users don't know or care about the history of speech in the office. They just buy it and use it, and they either like it (so far, so good) or they don't.

My only direct experience with speech interfaces was with the burgeoning automated call-center industry, which had been quite bad. But after learning more about the state of the art (Randy Allen Harris's Voice Interaction Design or Blade Kotelly's The Art and Business of Speech Recognition are excellent guides), I realized that there are many superb examples of voice interface design. It's a lot like Web sites and GUIs in the 1980s. The practice of human-centered user interface design was not widely known back then, but as the HCI discipline grew both in academia and industry, best practices spread. Products that didn't follow a good user-centered process were quickly displaced by competitors that did. There is an excellent set of user-centered design practices for speech interfaces that are very similar to the practices for core HCI. As yet, they aren't widely adopted, but the differences between systems that follow them and those that don't are so striking that this cannot last forever.

It has also become clear that the recognition accuracy of the ASR part of the interface is not the limiting factor - it's the quality of the overall VUI design and the match of the application to its context. In other words, there's no reason to wait for future technical magic before using speech interfaces. You can write excellent ones now, assuming speech interaction fits your application context. (See the recent examples that appeared in the article "'Conversational' Isn't Always What You Think It Is" from Speech Technology Magazine, July/August 2003; http://www.speechtechmag.com.)

After these epiphanies, I moved a significant amount of activity in my group to speech and dialog-based interfaces (i.e., started four new projects). While there are very good practices in speech interface design today and many useful services that can be built with them, there are still significant challenges and room for improvement. Those limits have to do with the shared understanding between a human and a machine sharing a speech interface. This is why speech interfaces are also a rich research area. Much of the shared information is the context we have already been talking about, and all of the aforementioned projects are coupled with our work on context-awareness (for more information, see my home page, http://www.cs.berkeley.edu/~jfc).

 

Perception

A Word (or Two) about Privacy

Perceptual interfaces imply cameras, microphones, and other sensors capturing the user's behavior. Context-awareness implies high-level interpretation of that data, often in locations remote (in space and time) from where the data was captured. These are all hot buttons for privacy advocates. My group has been working on context-aware systems for eight years, and privacy has always been an issue. In fact, privacy in ubiquitous computing environments has become a major focus of our group, leading to six papers on the topic. There are a variety of approaches to the problem: better advice and consent interfaces for users, anonymization, and various forms of obfuscation (e.g., reducing the accuracy of location information). I have co-organized workshops on privacy at the Ubiquitous Computing conference for the past four years (UBICOMP 2002-2005), and these have provided a good overview of work in the area (all are available from my home page).

Machine perception is a difficult task and it "scales" poorly: as you increase the size of the speech vocabulary or the number of potential images, accuracy goes down.

The approach we have taken, and which we are now building into a context-aware prototype, is private computation. In a private computation, user data is cryptographically protected during the computation, and only the final result is revealed. For example, we are interested in the overlap between activities of knowledge workers. It's possible to infer this overlap by discovering similar keywords in users' e-mails to each other. Normally, doing pattern matching on full e-mail text would be extremely invasive, but the result of the pattern matching is often benign by itself (e.g., if users A and B share a common activity, we typically need only the most salient words or documents related to that activity). Private computation allows us to determine the end result - say, the set of documents related to the activity - without exposing any information at all about the data used to do the pattern matching.

Private computation is challenging to use for a variety of reasons, one of which has been high computational cost. Our most recent result, however, has reduced this by many orders of magnitude and allows privacy to be added to many context algorithms with essentially no computational overhead (accessible as Berkeley Technical Report UCB/EECS-2006-12 from http://www.eecs.berkeley.edu/Pubs/TechRpts/2006/). This allows us to compute high-level context information, such as who is involved in an activity and how much (say, as a participation number between 0 and 1) without disclosing when and where the users were actually involved. Private computation provides much stronger privacy protection than anonymization - for example, e-mail with sender/receiver removed (anonymization) is hardly protected at all. Private computation requires some rather exotic techniques (zero-knowledge proofs), but we have built a Java toolkit that is available to others who would like to experiment with it.

Context-Awareness and Perception

Context-awareness and perception are really two sides of the same coin. Context-awareness involves interpreting other cues (besides user input) to figure out what a user wants. Many of these cues will require machine perception (is a user talking about food, is there traffic noise, is the sky overcast?). Conversely, machine perception is a difficult task and it "scales" poorly - as you increase the size of the speech vocabulary or the number of potential images to match for vision, accuracy goes down. The task becomes much easier when you add context data to the recognizer. In our research on face recognition, we were able to use available phone context data (time, place, event history) to improve recognition of faces from camera-phone images. In fact, face "recognition" using context data alone (i.e., predicting who's in the image without looking at it) was more accurate than a state-of-the-art face recognizer using computer vision. Putting computer vision and context together, though, does much better than either one alone.

Our work on voice interfaces is attempting to achieve similar gains by adding context data to speech recognition. We think the potential gains are even larger there. But there must be closer coupling between recognizer, the context data, and the application or service built on top of it. That brings us to what is realistically the biggest challenge to contextual and perceptual interfaces: bridging the barriers between the disciplines working on these technologies - specifically, HCI, speech recognition, and computer vision. It's a familiar story when there is a paradigm shift in a technology or market. While there are small communities working on the boundaries, most of the time recognizers are "black boxes" to interface developers. Conversely, folks working on recognition rarely pay attention to context or the applications that come later. We'll make some progress that way, but if we want a revolution, which the market is ready for, then we need to forget tribal allegiances and work together.

Overview of this Issue

The contributions in this issue cover the state of the art in perceptual and context-aware interfaces. In speech interfaces, one of the most exciting pieces of the market is the cellphone. Many cellphones now support speech input for speed dialing or selecting a name from the phone book. Large vocabulary interfaces for dictation appeared last year. Full continuous large-vocabulary recognition is on the way. The latter especially opens up whole new application possibilities for smart phones and may do much to break the usability barrier for these devices. Most of this technology was developed by VoiceSignal. We open this issue with an interview with Jordan Cohen, who recently moved to SRI, but was formerly the CTO of VoiceSignal. Wendy Kellogg, of IBM's Thomas J. Watson Research Lab, and I discuss with Cohen the growth of cellphone speech interfaces, their potential, and the challenges still remaining.

Our second article looks at computer vision-based interfaces. James Crowley, who directs the GRAVIR (Graphics, Vision, and Robotics) laboratory at INRIA (French National Institute for Research in Computer Science and Control) Rhône-Alpes in France, is a leader in this area. A major challenge in high-level interpretation of human actions is context, as we already noted. Crowley and his colleagues have tackled this problem head-on by developing a rich model of context considering "situations" and "scenarios." This article describes their approach top-down, starting with a representational model and drilling down to their software architecture.

In the third article, we look at context-awareness in a biology lab. Gaetano Borriello, computer science professor at the University of Washington, leads us through some field tests of the Labscape system, which is intended as an efficient but unobtrusive assistant (a Radar O'Reilly) for cell biologists. In this setting, the users' high-level activity is well understood (it's a science experiment). The system has to use available clues from sensing (like most context-aware systems, there is plenty of perception in this one) to figure out where the user is and what resources are needed. Borriello's article is rich with practical advice for making this kind of system succeed.

In our final article, Jim Christensen and colleagues from IBM's Thomas J. Watson Research Lab (including Wendy Kellogg) take a different approach to using context information. Whereas successful automatic context-aware systems are rare at this time, Christensen et al. argue for human interpretation of context information. They describe two systems that exemplify this approach: Grapevine, a system that mediates human-to-human communication to minimize inappropriate interruptions; and Rendezvous, a VoIP conference-calling solution that uses contextual information from corporate resources to enhance the user experience of audio conferencing. They also discuss some cogent issues related to user privacy in context-aware systems.

JOHN CANNY is the Paul and Stacy Jacobs Distinguished Professor of Engineering at the University of California, Berkeley. His research is in human-computer interaction, with an emphasis on behavior modeling and privacy. He received his Ph.D. in 1987 at the MIT AI Lab. His dissertation on Robot Motion Planning received the ACM dissertation award. He received a Packard Foundation Faculty Fellowship and a Presidential Young Investigator Award. His peer-reviewed publications span robotics, computational geometry, physical simulation, computational algebra, theory and algorithms, information retrieval, HCI and CSCW and cryptography. He has best-paper prizes in several of these areas.

for nisan

（旧文一篇，原贴于www.hereismusic.com “乐色撩人”）

乐色撩人


色情与音乐之间的关系本能地晦暗不明。当我们说到色情文学，色情绘画，色情舞蹈，色情电影，我们很明确地知道那指的是些什么东西。而色情音乐是什么东西？当然，色情歌曲是有的，但那是歌词而非音乐本身。《东方红》的曲调源于陕北的一支民歌，原来这曲子配的就是色情内容，在中国，每隔一个小时，《东方红》都会在广场、车站、电信大楼上响起，大概不会有人认为那是在传播色情。

有必要把艺术中的色情和色情艺术分开。《旧约·雅歌》、卜伽丘、劳伦斯、曹雪芹，他们对于情色的态度同黄色小说里表现出的态度是不一样的；提香、鲁本斯、比亚兹莱、克里木特同春宫画不一样……做这样的区别其实是迫不得已，我们的文明对于性爱总是遮遮掩掩、讳莫如深的态度反而让色情主题变得异常引人注目，因而在出现了两种不同的理解方式：一种是偷偷摸摸地窥视，把色情当作是来自地狱的诱惑，当作淫荡的妖女，窥视的快乐来源于想象犯罪的快感，劳伦斯称这样的态度是一种鼓励“手淫”的态度；另一种是大大方方地正视，把色情当作人类正常感性的一部分，健康明媚，意味无穷，温柔婉转或者惆怅感人，曹雪芹把贾宝玉称为“意淫”的情种，其实无非是指这样态度。

“真正的色情者不喜欢卜伽丘，因为这位意大利小说家清新、健康的自然风格使现代的色情小说家感到自己无异于一条可怜虫”，劳伦斯说，“……整个色情问题都是个隐秘的问题。一旦秘密揭穿，也就没什么色情可言。但隐秘和羞怯是截然不同的两样东西。”这段话点出两种态度的实质：隐秘其实暗含暴露的阴谋，越是隐秘就越要刺激人的阴暗心理；羞怯则是含蓄、优雅或者忧伤的，因为羞怯，所以象征、暗示和美丽的比喻（my love is a red red rose）就是不可或缺的。

音乐整体上说都是象征性的，即使是标题音乐也一样。所以可以说，音乐作品不会明确指向什么内容，不会用来“暴露”和“刺激”，而音乐却蕴涵着无穷丰富的意象和色彩，这些都可以认为与健康的情感和性爱有关。而且从接受角度而言，应该没人否认，含蓄朦胧的美要比暴露本身有更多的意味、更撩拨人的情感，好比女性身体最美的一刻不是赤裸而是薄纱轻笼。音乐同色情的关系既然本能地晦暗不明，那么从这层意义上讲，也可说是最具色情性的艺术。

在西方古典音乐里，我觉得最性感的音乐家是莫扎特。按照弗洛伊德老头儿的说法，艺术创作无非是“力比多”发泄，那么莫扎特区区30多年，居然完成了如此众多的音乐杰作，一定是“力比多”极端旺盛的人了。莫扎特对于色情，从气质上是接近卜伽丘和拉伯雷的，他的语言实在繁密丰富得可以，多半像是在边开着玩笑边写出来的。他的作品里有色情的内容，但这并不是最重要的，重要的是他音乐的情绪总是如此大胆，张扬，挑逗，一个人的作品里，同时具备激情与幽默，而且在他之前，没有人对音乐的创造性发挥到如此境界，这样的家伙自然是最性感的。

最色情的作品，我推举德彪西的“前奏曲集”和“意象集”。德彪西是最懂得朦胧美的音乐家，而且也是玩色彩的高手。他的钢琴音乐属于夜晚：月色，和风，以及怅然的心绪。略带忧郁的爱情最为动人，这两部钢琴作品深谙其中道理，难得的是“哀而不伤”，个性高贵而完整，音乐的气质有点接近普鲁斯特的文学，但没有后者病态。这两部作品通常被放在一起，用“绵绵细语”或“温柔的抚摩”来比喻最为贴切。

最色情的乐器，个人以为非钢琴莫数。道理还用讲么？什么样的意味不能用钢琴来表现？否则，餐厅、酒吧、咖啡屋这样的约会场所，为什么总要放架钢琴在那里？

音乐如果要同色情构成直接的联系，那就必须借助其他媒介了。好比歌词，舞蹈或者电影视像。不难理解，《雅歌》、《诗经》如果缺少了旋律，一定失色不少；酒神节的秘密狂欢，拉丁舞蹈的热辣，如果没有音乐更是无法完成；电影里面，以音乐来烘托效果，就要看具体手法如何了：《云上的日子》第一节故事结尾，男人对女人不触碰的抚摩镜头，我认为是电影史上表达色情最高超的一笔，手掌随着身体起伏而起伏，音乐随着动作而起伏，简直美到了极点；《亨利和琼》（情迷六月花）里，斯特拉文斯基的《春之祭》断续可闻，尽管没有被放在表现性爱的镜头中运用，但毫无疑问为整个电影定下了情色的主题色调。这些音乐的色情意味都没的说，被突出得恰倒好处。