Meet the robot guitarist with 78 finger spelling

17 best Music 3Dified! images on Pinterest in | Musical Instruments, Guitar and Guitars

meet the robot guitarist with 78 finger spelling

This just happened a week ago. You can now buy online from Gibson without being a certified dealer from any of the 3 factories: Memphis. James Marshall Hendrix was an American rock guitarist, singer, and songwriter. Although his .. Hendrix met guitarist Noel Redding at an audition for the New Animals, Chandler also convinced Hendrix to change the spelling of his first name left hand would play and the other fingers playing melody as a right hand. To use the fingers as part of a 3D gestural interface, we need to track .. hierarchical model for recognition of signs and finger spelling [73]. for recognizing body and hand gestures for aircraft signal handling [78]. .. recognition technology used in human robot interaction applications include [ –].

Joseph, born inKathy inand Pamela,all of whom Al and Lucille gave up to foster care and adoption. On occasion, family members would take Hendrix to Vancouver to stay at his grandmother's.

A shy and sensitive boy, he was deeply affected by his life experiences. After more than a year of his clinging to a broom like a security blanketshe wrote a letter requesting school funding intended for underprivileged children, insisting that leaving him without a guitar might result in psychological damage.

Jerry Garcia - Wikipedia

She told him that he could keep the instrument, which had only one string. KingHowlin' Wolfand Robert Johnson. Without an electric guitar, he could barely be heard over the sound of the group. After about three months, he realized that he needed an electric guitar.

When his guitar was stolen after he left it backstage overnight, Al bought him a red Silvertone Danelectro. Given a choice between prison or joining the Armyhe chose the latter and enlisted on May 31, They work you to death, fussing and fighting. Rich awarded him the prestigious Screaming Eagles patch on January 11, They labeled him an unqualified marksman and often caught him napping while on duty and failing to report for bed checks.

Spears, filed a report in which he stated: It is my opinion that Private Hendrix will never come up to the standards required of a soldier. I feel that the military service will benefit if he is discharged as soon as possible. Down there you have to play with your teeth or else you get shot. There's a trail of broken teeth all over the stage.

He moved into the Hotel Theresa in Harlemwhere he befriended Lithofayne Pridgon, known as "Faye", who became his girlfriend.

At the recommendation of a former associate of Joe TexRonnie Isley granted Hendrix an audition that led to an offer to become the guitarist with the Isley Brothers ' back-up band, the I. Specials, which he readily accepted. Released in June, it failed to chart. Issued in August by Rosemart Records and distributed by Atlanticthe track reached number 35 on the Billboard chart. The single failed to chart, but Hendrix and Lee began a friendship that lasted several years; Hendrix later became an ardent supporter of Lee's band, Love.

The video recording of the show marks the earliest known footage of Hendrix performing. They failed to see Hendrix's musical potential, and rejected him. Mitchell, who had recently been fired from Georgie Fame and the Blue Flamesparticipated in a rehearsal with Redding and Hendrix where they found common ground in their shared interest in rhythm and blues.

When Chandler phoned Mitchell later that day to offer him the position, he readily accepted. I said, 'Of course', but I had a funny feeling about him. I mean he did a few of his tricks, like playing with his teeth and behind his back, but it wasn't in an upstaging sense at all, and that was it Other 3D gesture recognizers that make use of CRFs include [ 397475 ]. Hidden conditional random fields HCRFs extend the concept of the CRF by adding hidden state variables into the probabilistic model which is used to capture complex dependencies in the observations while still not requiring any independence assumptions and without having to exactly specify dependencies [ 76 ].

In other words, HCRFs enable sharing of information between labels with the hidden variables but cannot model dynamics between them. HCRFs have also been utilized in 3D gesture recognition. For example, Sy et al. This approach builds upon the HCRF by providing the ability to model the substructure of a gesture label and learn the dynamics between labels, which helps in recognizing gestures from unsegmented data [ 82 ].

They examined different window sizes and used location, orientation, and velocity features as input to the recognizers, with LDCRFs performing the best in terms of recognition accuracy [ 86 ]. Support Vector Machines Support vector machines SVMs are another approach that is used in 3D gesture recognition that has received considerable attention in recent years.

SVMs are a supervised learning-based probabilistic classification approach that constructs a hyperplane or set of hyperplanes in high dimensional space used to maximize the distance to the nearest training data point in a given class [ 87 ].

meet the robot guitarist with 78 finger spelling

These hyperplanes are then used for classification of unseen instances. The mappings used by SVMs are designed in terms of a kernel function selected for a particular problem type.

Jimi Hendrix - Wikipedia

Since not all the training data may be linearly separable in a given space, the data can be transformed via nonlinear kernel functions to work with more complex problem domains. In terms of 3D gestures, there have been many recognition systems that make use of SVMs. For example, recent work has explored different ways of extracting the features used in SVM-based recognition.

This vector was used as the input to a multiclass SVM [ 9192 ]. For example, Chen and Tseng used 3 SVMs from 3 different camera angles to recognize 3D hand gestures by fusing the results from each with majority voting or using recognition performance from each SVM as a weight to the overall gesture classification score [ 93 ].

The results from these two classifiers were then combined to provide a more general recognition framework [ 94 ]. Other 3D gesture recognizers that utilize SVMs include [ 8095 — ]. Decision Trees and Forests Decision trees and forests are an important machine learning tool for recognizing 3D gestures. With decision trees, each node of the tree makes a decision about some gesture feature. The path traversed from the root to a leaf in a decision tree specifies the expected classification by making a series of decisions on a number of attributes.

There are a variety of different decision tree implementations [ ]. One of the most common is the C4. This strategy is used in the construction of the decision tree. In the context of 3D gesture recognition, there have been several different strategies explored using decision trees. For example, Nisar et al. They added a fuzzy element to their approach, developing a multivariate decision tree learning and classification algorithm. This approach uses fuzzy membership functions to calculate the information gain in the tree [ ].

They used a 3-axis accelerometer and electromyography EMG sensors as input to the recognizer [ ]. Other examples of using decision trees in 3D gesture recognition include [].

Decision forests are an extension of the decision tree concept.

Jerry Garcia

The main difference is that instead of just one tree used in the recognition process, there is an ensemble of randomly trained decision trees that output the class that is the mode of the classes output by the individual trees [ ]. Given the power of GPUs, decision forests are becoming prominent for real-time gesture recognition because the recognition algorithm can be easily parallelized with potentially thousands of trees included in the decision forest [ ].

This decision forest approach can be considered a framework that has several different parts that can produce a variety of different models. The shape of the decision to use for each node, the type of predictor used in each leaf, the splitting objective used to optimize each node, and the method for injecting randomness into the trees are all choices that need to be made when constructing a decision forest used in recognition. One of the most notable examples of the use of decision forests was Shotton et al.

This work led researchers to look at decision forests for 3D gesture recognition. For example, Miranda et al. Key poses from the skeleton data are extracted using a multiclass SVM and fed as input to the decision forest. A realistic 3D hand model with 21 different parts was used to create synthetic depth images for decision forest training. In another example, Negin et al. These features are then fed into a SVM for gesture recognition.

Other work that has explored the use of decision forests for 3D gesture recognition include [, ]. Other Learning-Based Techniques There are, of course, a variety of other machine learning-based techniques that have been used for 3D gesture recognition, examples include neural networks [], template matching [], finite state machines [], and using the Adaboost framework [ ].

To cover all of them in detail would go beyond the scope of this paper. However, two other 3D gesture recognition algorithms are worth mentioning because they both stem from recognizers used in 2D pen gesture recognition, are fairly easy to implement, and provide good results. These recognizers tend to work for segmented data but can be extended to unsegmented data streams by integrating circular buffers with varying window sizes, depending on the types of 3D gestures in the gesture set and the data collection system.

This classifier is a linear discriminator where each gesture has an associated linear evaluation function, and each feature has a weight based on the training data. The classifier uses a closed form solution for training which produces optimal classifiers given that the features are normally distributed.

However, the approach still produces good results even when there is a drift from normality. This approach also always produces a classification so the false positive rate can be high. However a good rejection rule will remove ambiguous gestures and outliers. The extension of this approach to 3D gestures is relatively straightforward. The features need to be extended to capture 3D information with the main classifier and training algorithm remaining the same. This approach has been used successfully in developing simple, yet effective 3D gesture recognizers [, ].

The second approach is based on Wobbrock et al. In this approach, gesture traces are created using the differences between the current and previous acceleration data values and resampled to have the same number of points as any gesture template. A heuristic scoring mechanism is used to help reject false positives. Note that a similar approach to constructing a 3D gesture recognizer was done by Li, who adapted the Protractor 2D gesture recognizer [ ] and extended it to work with accelerometers and gyroscope data [].

Heuristic Recognizers Heuristic 3D gesture recognizers make sense when there are a small number of easily identifiable gestures in an interface. The advantage of heuristic-based approaches is that no training data is needed and they are fairly easy to implement.

meet the robot guitarist with 78 finger spelling

For example, Williamson et al. An example of a heuristic recognizer for jumping would be to assume a jump was made when the head is at a certain height above its normal position, defined as where is true or false based on if a jump has occurred, is the height of the head position, is the calibrated normal height of the head position with the user standing, and is some constant. Such recognition is very specialized but simple and explainable and can determine in an instant whether a jump has occurred.

For example, One Man Band used a Wiimote to simulate the movements necessary to control the rhythm and pitch of several musical instruments [ ]. RealDance explored spatial 3D interaction for dance-based gaming and instruction [ ]. These explorations led to several heuristic recognition schemes for devices which use accelerometers and gyroscopes.

Poses and Underway Intervals. A pose is a length of time during which the device is not changing position. Poses can be useful for identifying held positions in dance, during games, or possibly even in yoga. An underway interval is a length of time during which the device is moving but not accelerating. Underway intervals can help identify smooth movements and differentiate between, say, strumming on a guitar and beating on a drum.

Because neither poses nor underway intervals have an acceleration component, they cannot be differentiated using accelerometer data alone. To differentiate the two, a gyroscope can provide a frame of reference to identify whether the device has velocity. Alternatively, context can be used, such as tracking acceleration over time to determine whether the device is moving or stopped. Poses and underway intervals have three components. First, the time span is the duration in which the user maintains a pose or an underway interval.

Second, the orientation of gravity from the acceleration vector helps verify that the user is holding the device at the intended orientation. Third, the allowed variance is the threshold value for the amount of acceleration allowed in the heuristic before rejecting the pose or underway interval.

For example, in RealDance [ ], poses were important for recognizing certain dance movements. For a pose, the user was supposed to stand still in a specific posture beginning at time and lasting untilwhere is a specified number of beats. An impulse motion is characterized by a rapid change in acceleration, easily measured by an accelerometer. A good example is a tennis or golf club swing in which the device motion accelerates through an arc or a punching motion, which contains a unidirectional acceleration.

An impulse motion has two components, which designers can tune for their use.


First, the time span of the impulse motion specifies the window over which the impulse is occurring. Shorter time spans increase the interaction speed, but larger time spans are more easily separable from background jitter. The second component is the maximum magnitude reached.

This is the acceleration bound that must be reached during the time span in order for the device to recognize the impulse motion. Impulse motions can also be characterized by their direction. The acceleration into a punch is essentially a straight impulse motion, a tennis swing has an angular acceleration component, and a golf swing has both angular acceleration and even increasing acceleration during the follow-through when the elbow bends.

All three of these impulse motions, however, are indistinguishable to an acceleration only device, which does not easily sense these orientation changes. For example, the punch has an acceleration vector along a single axis, as does the tennis swing as it roughly changes its orientation as the swing progresses.

These motions can be differentiated by using a gyroscope as part of the device or by assuming that orientation does not change. As an example, RealDance used impulse motions to identify punches.

A punch was characterized by a rapid deceleration occurring when the arm was fully extended. In a rhythm-based game environment, this instant should line up with a strong beat in the music. An impulse motion was scored by considering a one-beat interval centered on the expected beat. An impact event is an immediate halt to the device due to a collision, characterized by an easily identifiable acceleration bursting across all three dimensions.

Examples of this event include the user tapping the device on a table or dropping it so it hits the floor. To identify an impact event, the change in acceleration jerk vector is required for each pair of adjacent time samples. Here, corresponds to the largest magnitude of jerk: If the magnitude is larger than a threshold value, an impact occurs.

As an example, RealDance used impact motions to identify stomps. If the interval surrounding a dance move had a maximal jerk value less than a threshold, no impact occurred. One Man Band also used impact events to identify when a Nintendo Nunchuk controller and Wiimote collided, which is how users played hand cymbals. Herustics can also be used as a form of simple segmentation to support the recognition of different gestures.

Figure 3 shows four of these. If the user held the Wiimote on its side and to the left, as if playing a guitar, the application interpreted impulse motions as strumming motions. If the user held the Wiimote to the left, as if playing a violin, the application interpreted the impulse motions as violin sounds.

The second function, with anremoved jitter and identified short, sharp gestures such as violin strokes. One Man Band differentiated between multiple Wiimote gestures using mostly simple modal differentiations for a drums, b guitar, c violin, and d theremin.

To the player, changing instruments only required orienting the Wiimote to match how an instrument would be played. Experimentation and Accuracy As we have seen in the last section, there have been a variety of different approaches for building 3D gesture recognition systems for use in 3D gestural interfaces.

meet the robot guitarist with 78 finger spelling

In this section, we focus on understanding how well these approaches work in terms of recognition accuracy and the number of gestures that can be recognized. These two metrics help to provide researchers and developers guidance on what strategies work best. As with Section 3we do not aim to be an exhaustive reference on the experiments that have been conducted on 3D gesture recognition accuracy.

Rather, we present a representative sample that highlights the effectiveness of different 3D gesture recognition strategies. A summary of the experiments and accuracy of various 3D gesture recognition systems is shown in Table 1. This table shows the authors of the work, the recognition approach or strategy, the number of recognized gestures, and the highest accuracy level reported. However, the number of gestures in the gesture sets used in the experiments vary significantly.

The number of gestures in the gesture set is often not indicative of performance when comparing techniques. In some cases, postures were used instead of more complex gestures and in some cases, more complex activities were recognized. For example, Lee and Cho recognized only 3 gestures, but these are classified as activities that included shopping, taking a bus, and moving by walking [ 61 ].

The gestures used in these actions are more complex than, for example, finger spelling. In other cases, segmentation was not done as part of the recognition process. For example, Hoffman et al. A table summarizing different 3D gesture recognition approaches, the size of the gesture set, and the stated recognition accuracy.

It is often difficult to compare 3D gesture recognition techniques for a variety of reasons including the use of different data sets, parameters, and number of gestures. However, there have been several, more inclusive experiments that have focused on examining several different recognizers in one piece of research.

For example, Kelly et al. They compared the nearest neighbor algorithm with nested generalization, naive Bayes, C4. Finally, Cheema et al. They found that the linear classifier performed the best under different conditions which is interesting given its simplicity compared to the other 3D gesture recognition methods. However, SVM and AdaBoost also performed well under certain user independent recognition conditions when using more training samples per gesture.

Experiments on 3D gesture recognition systems have also been carried out in terms of how they can be used as 3D gestural user interfaces and there have been a variety of different application domains explored [ ]. Entertainment and video games are just one example of an application domain where 3D gestural interfaces are becoming more common.

This trend is evident since all major video game consoles and the PC support devices that capture 3D motion from a user. In other cases, video games are being used as the research platform for 3D gesture recognition.

Figure 4 shows an example of using a video game to explore what the best gesture set should be for a first person navigation game [ 9 ], while Figure 5 shows screenshots of the video game used in Cheema et al.

Other 3D gesture recognition research that has focused on the entertainment and video game domain include [— ]. A user performing a gesture in a video game application [ 9 ]. Screenshots of a video game used to explore different 3D gesture recognition algorithms [ ]. Medical applications and use in operating rooms are an area where 3D gestures have been explored.

Using passive sensing enables the surgeon or doctor to use gestures to gather information about a patient on a computer while still maintaining a sterile environment []. For example, Pfeil et al. They developed and evaluated several 3D gestural metaphors for teleoperating the robot.

Other examples of 3D gesture recognition technology used in human robot interaction applications include [ — ].

meet the robot guitarist with 78 finger spelling

Other application areas include training and interfacing with vehicles. Finally, 3D gesture recognition has recently been explored in consumer electronics, specifically for control of large screen smart TVs []. A user controlling a UAV using a 3D gesture [ ].

Future Research Trends Although there have been great strides in 3D gestural user interfaces from unobtrusive sensing technologies to advanced machine learning algorithms that are capable of robustly recognizing large gesture sets, there still remains a significant amount of future research that needs to be done to make 3D gestural interaction truly robust, provide compelling user experiences, and support interfaces that are natural and seamless to users.

In this section, we highlight three areas that need to be explored further to significantly advance 3D gestural interaction. Customized 3D Gesture Recognition Although there has been some work on customizable 3D gestural interfaces [ ], customization is still an open problem. Customization can take many forms and in this case, we mean the ability for users to determine the best gestures for themselves for a particular application.

Users should be able to define the 3D gestures they want to perform for a given task in an application. This type of customization goes one step further than having user-dependent 3D gesture recognizers although this is still a challenging problem in cases where many people are using the interface.

There are several problems that need to be addressed to support customized 3D gestural interaction. First, how do users specify what gestures they want to perform for a given task. Second, once these gestures are specified, if using machine learning, how do we get enough data to train the classification algorithms without burdening the user?

Ideally, the user should only need to specify a gesture just once. This means that synthetic data needs to be generated based on user profiles or more sophisticated learning algorithms that deal with small training set sized are required. Third, how do we deal with user defined gestures that are very similar to each other? This problem occurs frequently in all kinds of gestures recognition, but the difference in this case is that the users are specifying the 3D gesture and we want them to use whatever gesture they come up with.

These are all problems that need to be solved in order to support truly customized 3D gestural interaction.

meet the robot guitarist with 78 finger spelling

Latency 3D gesture recognition needs to be both fast and accurate to make 3D gestural user interfaces usable and compelling. In fact, the recognition component needs to be somewhat faster than real time because responses based on 3D gestures need to occur at the moment a user finishes a gesture. Thus, the gesture needs to be recognized a little bit before the user finishes it. This speed requirement makes latency an important problem that needs to be addressed to ensure fluid and natural user experiences.

Latency can be broken up into computational latency and observational latency [ 74]. Computational latency is the delay that is based on the amount of computation needed to recognize 3D gestures. Observational latency is the delay based on the minimum amount of data that needs to be observed to recognize a 3D gesture.

Both latencies present an important area in terms of how to minimize and mitigate them.